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One of the most common issues I see when people deploy SIP is calls hanging up after approximately 30 seconds or traffic not going to where it should. This can be hard for users to grasp and is primarily due to the fact that SIP embeds routing information (IP addresses and ports) within the signaling itself. When SIP was originally created this would have been perfectly fine but in a day and age where NAT is prevalent and the IP address and port may be internal, issues can arise. Let’s take a look at the basic areas which are applicable to most people!
The Via header in a SIP message shows the path that a message took, and determines where responses should be sent to. By default in Asterisk we send to the source IP address and port of the request, overcoming any NAT issues. There are some devices, however, that this does not work properly with. An example is some Cisco phones that require you send responses to the port provided in the Via header. This can be accomplished in chan_pjsip by setting the “force_rport” option to “no” on the endpoint.
The Contact header in a SIP message provides a target for where subsequent requests should be sent to. The Contact header is present in calls, registrations, subscriptions, and more. As you might expect when a device is behind NAT it might not know its public IP address and port and would instead place its private IP address and port in the Contact header. If a SIP device receives this header and is not on the same network it would be unable to contact the device. In a call scenario this exhibits itself upon answering a call. A 200 OK with a Contact header is sent to indicate that the call is answered and the other party then sends an ACK message to the target in the Contact header. If this is not received the 200 OK will be retransmitted until the sender gives up and terminates the call generally after approximately 30 seconds. The chan_pjsip module provides the “rewrite_contact” option to overcome this. It changes the received Contact header to be the actual source IP address and port of the SIP request and effectively ignores what the other party stated.
SDP c= and m= Lines
Media is not immune to NAT as many people likely know. Just like SIP signaling the IP address and port for where media should be sent to is also exchanged in SDP in the “c=” and “m=” lines. Just like with the Contact header a device may not put the correct information in resulting in media being sent to the wrong target. This can be resolved using the “rtp_symmetric” option in chan_pjsip. This configuration option instructs the Asterisk RTP implementation to latch on to the source of media it receives and send outgoing media to that target instead, ignoring what was presented in the “c=” and “m=” lines.
I hope this has provided a bit of insight into a very common problem that people see, why it occurs, and how to resolve it. You’ll note I haven’t covered if Asterisk is behind NAT but instead focused on SIP in general and for devices behind NAT. Don’t despair as there is an excellent wiki page which covers that subject. |
Compromised credentials and malware are the top two attacker methodologies according to the 2014 Verizon Data Breach Investigations Report. While UserInsight focuses primarily on detecting compromised credentials, a huge gap in most security programs, UserInsight now helps detect malware on endpoints in your entire organization Ð without having to deploy any software to the endpoints.
Protect your endpoints with the wisdom of 50 virus scanners and the footprint of none
UserInsight checks each process against a database of malware scanning results of over 50 virus scanners and alerts if the process is reported to be malicious. While individual anti-virus scanners will always have blind spots, installing several scanners on the endpoint is not an option because they would conflict with each other and grind performance to a halt. UserInsight leverages the wisdom of more than 50 virus scanners by checking processes against a database of previous scanning results, protecting UserInsight subscribers against malware as soon as malware vendors detect a new piece of malware.
UserInsight customers who have piloted this new functionality have already reported successes. They detected mass malware on their endpoints that had previously remained undetected by their existing virus scanners.
Individual virus scanners not only have blind spots but also false positives. This is why UserInsight enables organizations to set thresholds of how many virus scanners must flag a process as malicious before it is being reported as an alert, helping us reduce the false positive rate and alert fatigue.
Some types of malware run under the names of legitimate processes to avoid detection. UserInsight takes a hash of the process to help detect these kinds of malware as well.
The endpoint monitoring does not require the deployment or management of a software agent to the endpoints, which can be a burden for overworked IT organizations. UserInsight achieves this through credentialed scanning of endpoints, greatly reducing the amount of overhead for monitoring endpoints. The new endpoint malware detection works with both Windows and Mac operating systems.
New endpoint malware detection builds on existing malware functionality
The new endpoint malware detection methods build on UserInsight's existing capability to detect malicious processes.
- Rare and unique processes: While the new functionality extends the detection to known mass malware, UserInsight already gave customers visibility of malware that uses polymorphism or malware that was customized for a targeted attack. Custom or obfuscated malware stands out as an anomaly when compared to other processes that run in an organization. For example, an office application would be present on thousands of machines in an organization, while a piece of malware would only show on one or two. In addition, legitimate processes are often digitally signed by an organization. UserInsight detects unsigned rare and unique processes in an organization to help incident responders detect these types of targeted attacks.
- User context for advanced malware: Advanced malware solutions use sandboxes to scrutinize executables and files for malicious behavior. Because organizations are afraid of false positive alerts impacting the productivity of their users, most IT security teams deploy advanced malware solutions only in detection mode without blocking emails or web access. As a result, alerts must be closely monitored and investigated. However, it can be difficult to investigate an attack given only the IP address of a machine that caused an alert, especially in environments with dynamic IP addresses. UserInsight has existing integrations with FireEye NX Series and Palo Alto Wildfire to help incident responders easily identify the user connected to an alert and provides the full context of activities of that user to accelerate the investigation.
- Adding alerts from endpoint protection platforms to investigations: Endpoint protection platforms are typically set up to quarantine malware, so they are rarely centrally monitored because there is no follow-up required. UserInsight provides malware alerts from endpoint protection platforms to provide more context in incident investigations. For example, let's assume an intruder tries three times to phish a user Ð the first two attempts are blocked by the virus scanner, but the third attempt goes through. In an investigation, the endpoint protection platform would report the first two blocked attempts, providing useful context about the initial attack vector.
How to set up UserInsight to detect malware on endpoints
Using the malware endpoint detection with UserInsight is very easy. If you are already using the endpoint monitoring, you will see 'MALICIOUS PROCESS ON ASSET' alerts showing up in your incident alerts.
If you don't have endpoint monitoring set up yet, here is how you do it:
- Go to the Collectors page in UserInsight.
- Click on 'Rapid7' in the event sources list on the left.
- Click the sign on the collector for the location where you'd like to add endpoint scanning.
- Select 'Rapid7 Endpoint Monitor' for Windows or 'Rapid7 Mac Endpoint Monitor' for Mac endpoints and ensure that you activate the dissolvable agent.
The new functionality to detect malicious processes is available immediately. If you'd like to test it out, please contact us to schedule a 1:1 demo or talk about evaluating UserInsight. |
#include <log/chLog.h>void sysLog(const char *format, ... /* arg */);
The function or functions documented here may not be used safely in all application contexts with all APIs provided in the ChorusOS 5.0 product.
See API(5FEA) for details.
The sysLog() call logs a message in the microkernel's cyclical buffer. The syntax of sysLog() is similar to that of printf() with the restriction that the only conversion specifications supported are "%s", "%d", and "%c".
The formatted string is truncated to SYSLOG_MAX_LINE characters, as defined in <log/chLog.h>.
See attributes(5) for descriptions of the following attributes:
|ATTRIBUTE TYPE||ATTRIBUTE VALUE|
Important actors related to system administration use sysLog(), for example pppstart.r, slattach.r and chat.r.
host% chls -ll nblines
host% rsh target arun /bin/cs -ll nblines
where nblines is the number of lines to display counting from the end of the system log, and target is the target system hostname. |
Secure Telephony Identity Revisited is an innovative group which gathers information about spoof callers and calls. This group, also known as “STIR” attempts to solve various issues involving phone identifications and identifiers. There are many types of phone identification frauds that exist such as phishing and robot type calls. These calls may involve harassment and promote aggressive style tactics such as repeat calling. Essentially, STIR attempts to separate the veritable substance calls from the spoof calls.
Read the full article here: |
The Scenario-based questions cover the following Learning Outcomes:
2. Apply data recovery techniques to forensic investigation in the network and mobile environments.
4. Apply forensic methodology to digital corporate and crime investigation in an ethical and professional context and employ appropriate technically writing skills in its report presentation.
You’re an analyst at a Singapore manufacturing corporation named WoW Pvt. Ltd. On Wednesday 2015-08-05, you saw some alerts while working at the corporation’s Security Operations Center Department.
While investigation, your team contacts one of the suspected employee, who is not aware of the suspicious files found on his desktop.
The Network administrator helps to retrieve a pcap of traffic for the timeframe of the alerts and the HTTPS traffic logs for that IP address. Another analyst searches the company’s mail servers and retrieves four malicious emails that might be related.
You now have
Network.pcap – a pcap of the traffic,
HTTPS traffic logs,
a collection of artifacts from that HTTPS traffic, and
malicious emails the suspected employee received during that timeframe.
The scope of DF’s investigation covers :
Analyze the Network.pcap (packet capture) files that were captured by the network administrator at WoW Pvt. Ltd.
Conduct an interview with the alleged employee and general manager of WoW Pvt. Ltd. Take statements from both parties.
Conduct digital investigation into the alleged employee’s mobile device (corporate-issued) and corporate computing device (workstation).
Technically evaluate the corporate email server logs in lieu of the footprints of the alleged employee’s computing and mobile devices.
Figure out how the computer became infected and document your findings. Your report should include:
List down the name of protocols used in the given pcap.
List the required protocols to be analyzed for the given case.
The IP Address of the computer where you found the alerts??
Who used this computer?
The infected computer’s hostname.
The infected computer’s MAC address.
The infected computer’s operating system.
The date, time, subject line, and sender of the malicious email that caused the infection.
Information on any malware associated with the infection.
Domains and IP addresses of any related traffic.
A timeline of events leading to the infection.
How you did the Malware Analysis
DRADFA Forensics is not investigating any other devices nor interviewing other parties aside from those mentioned.
Mr. Lim is the WoW’s general manager (GM). He is the client of DRADFA Forensics with you as the assigned forensic investigator.
Analyze the digital evidence and recommend if the alleged employee had any role in Malware found on the company’s mail server.
Figure out how the computer became infected and document your findings.
Research, critically analyze, and purposely propose the following for your approach to the forensics investigation:
Planning consideration and procedures to adopt for investigation
Technical Tools (hardware, software) to use for acquisition and analysis
Technical recommendations for analysis and considerations
Procedures & Guidelines for interviews and considerations
Considerations for documentation (forms, templates) and reporting |
Symantec Mail Security 5.0 for SMTP is not blocking spam email. When you examine the email that is passing through the filter, you notice that it contains HTML tags. One method used by spammers to propagate their spam past filters is the use of HTML comment tags inserted between the letters of words that normally trigger an action. This technique circumvents the simple word-searching capabilities of modern scanners. To combat this, use complex regular expressions when scanning email for spam.
Due to the complexity of regular expressions, Symantec Technical Support does not have the resources to troubleshoot compliance rules that use regular expressions. The following steps are unsupported and are provided for your convenience.
Before you begin:
- Make sure that the user name with which you logged in is a member of the Symantec Mail Security for SMTP Admins security group.
- Symantec Mail Security 5.0 for SMTP cannot open password-protected archives or archives that use encryption.
- Archive files that use an incorrect extension do not open properly.
To filter spam that uses HTML Comment Tags, create a regular expression rule that searches mail for instances of HTML comments. You can accomplish this in one of two ways.
Block every email that contains HTML Comment Tags
The advantage to this method is the ease of implementation. However, this method could have a high false-positive rate. The following is the format for creating an expression to block every email containing an HTML Comment Tag:
"<!-- Converted from text/plain format -->"
Note: Testing revealed that some email client software tags valid email with HTML comments.
To create a compliance policy containing every spam word to be blocked, and paste the regular expression between each letter
The advantage of this technique is the accuracy and low number of false-positives. The disadvantage is the unwieldy implementation, as the regular expression needs to be between every letter of each word, and requires a separate condition for each word. The following two methods are examples of implementing this solution:
To configure Symantec Mail Security 5.0 for SMTP to block spam which uses HTML tags, you must:
- Create a compliance filtering policy which filters for specific terms as specified within the policy itself.
- Test the rule.
To create a filtering rule
- In the Symantec Mail Security 5.0 for SMTP user interface, on the Policies tab, click Compliance.
- Click Add.
- In the Policy name text field, type:
Block HTML tags with regular expressions
- Under Apply to, select Inbound messages.
- Under Apply to the following policy groups, check Groups to select all groups.
- Under If the following conditions are met, select Body.
- Click matches regular expression button.
- In the box beside matches regular expression, type the word you seek to check for HTML Comment tags.
- Paste the following regular expression between each letter of the word you are checking for HTML Comment Tags:
For example if your compliance policy contains the word: quack
Note: Bolding of letters for emphasis only. You do not need to bold the letters when creating your compliance policy.
- Click Add Condition.
- Repeat steps 6 through 10 for each additional word you seek to check for HTML Comment tags.
- Under Perform the following action, select Hold message in Spam Quarantine.
- Click Save.
- Email containing any of the words 'quack', or 'squack', are now be blocked whether they have HTML comments inserted in-between the letters or not.
To test the new rule
- Create a message with a subject line that contains one of the terms which violate the rule.
- Send this message into the test network from an external account, and monitor the results.
If the message is placed in the Spam Quarantine, the rule works.
- If necessary, add or refine actions and retest by sending another message from an external account.
- Add the rule and match list to your production environment.
Symantec recommends that you test every new policy or modified policy to make sure that it works as you expect. A test network allows more control over the test process, and email generally travels more quickly through the system.
Detailed information regarding regular expressions can be found on page 91 of the Symantec Mail Security for SMTP Implementation Guide.
- Perl regular expressions
- General description of compliance policy behavior within Symantec Mail Security 5.0 for SMTP |
Object detection is an advanced computer vision technique that enables the identification and localization of objects within an image or video stream. It has a wide range of practical applications, including video surveillance, self-driving cars, and image search. Object detection algorithms are capable of identifying objects of interest, such as people, animals, and vehicles, and tracking their movements within a video feed. In this article, we will explore the theory behind object detection networks and how they can be used to create powerful video surveillance systems like Object Detection software.Object detection networks typically consist of two main components: a feature extractor and a classifier. The feature extractor is responsible for analyzing an input image or video frame and extracting a set of high-level features that can be used to identify objects. These features might include edges, corners, textures, and colors. The classifier, on the other hand, is responsible for determining the presence and location of objects within the image or video frame. It does this by analyzing the extracted features and comparing them to a set of pre-defined object categories.One popular approach to object detection is the use of deep learning algorithms, particularly convolutional neural networks (CNNs). CNNs are a type of artificial neural network that is designed to process visual data, such as images and videos. They consist of multiple layers of interconnected neurons that are capable of learning complex patterns within the data.In the context of object detection, a CNN is typically trained on a large dataset of annotated images. During the training process, the network learns to identify the unique features associated with each object category. These features might include the shape of a persons face, the color of a car, or the texture of an animals fur. Once the network has been trained, it can be used to classify new images and videos in real-time.Object detection networks can be used to create powerful video surveillance systems like Object Detection software. These systems are capable of monitoring multiple cameras simultaneously and detecting the presence of objects of interest, such as people, animals, and vehicles. When an object is detected, the system can automatically trigger a recording and upload the video to a cloud-based storage system for later review. These systems can also be used for automatic face recognition, allowing authorized individuals to be identified and tracked within a video feed.In conclusion, object detection networks are an essential tool for creating advanced video surveillance systems. They allow for the automatic identification and localization of objects within a video feed, enabling real-time monitoring and recording. These systems can be used in a wide range of applications, from home security to self-driving cars. As computer vision technology continues to advance, we can expect to see even more advanced object detection systems in the future. |
Authentication, Authorization & Accounting
The course is part of these learning pathsSee 2 more
Cloud Security is a huge topic, mainly because it has so many different areas of focus. This course focuses on three areas that are fundamental: AWS Authentication, Authorization, and Accounting.
These three topics can all be linked together and having an understanding of the different security controls from an authentication and authorization perspective can help you design the correct level of security for your infrastructure. Once an identity has been authenticated and is authorized to perform specific functions it's then important that this access can be tracked with regards to usage and resource consumption so that it can be audited, accounted, and billed for.
The course will define and discuss each area, and iron out any confusion of meaning between various security terms. Some people are unaware of the differences between authentication, authorization, and access control, this course will clearly explain the differences here allowing you to use the correct terms to describe your security solutions.
From an AWS authentication perspective, a number of different mechanisms are explained, such as Multi-Factor AWS Authentication (MFA), Federated Identity, Access Keys, and Key Pairs. With the help of demonstrations, you can learn how to apply access keys to your AWS CLI for programmatic access and understand the differences between Linux and Windows authentication methods using AWS Key Pairs.
When we dive into understanding authorization we cover IAM Users, Groups, Roles, and Policies, providing examples and demonstrations. Within this section, S3 authorization is also discussed, looking at access control lists (ACLs) and Bucket Policies. Moving on from S3, we look at network- and instance-level authorization with the help of Network Access Control Lists (NACLs) and Security Groups.
Finally, the Accounting section will guide you through the areas of Billing & Cost Management that you can use to help identify potential security threats. In addition to this, we explain how AWS CloudTrail can be used to track API calls to analyze what users are doing and when. This makes CloudTrail a strong tool in tracking, identifying, and monitoring a user's actions within your AWS environment.
- Obtain a strong grasp of the difference between authentication, authorization, access control, and accounting
- Understand various authentication mechanisms used in AWS such as MFA, Federated Identity, Access Keys, and Key Pairs
- Learn about IAM Users, Groups, Roles, and Policies and how they tie into authorization in AWS
- Learn about billing and cost management, and how to use it to identify potential security threats
- Understand how AWS CloudTrail can be used to track, identify, and monitor users' actions within AWS
This course has been created for anyone with an interest in cloud security, and/or who may hold a position of cloud solutions architect, cloud security specialist, or similar.
To get the most out of this course, you should have a basic understanding of identity and access management (IAM), Amazon EC2, Amazon S3 storage, networking fundamentals, and the virtual private cloud service.
Hello, and welcome to this lecture, discussing how authorization can be granted within AWS.
In an earlier lecture, I discussed the differences between authentication and authorization, and I just want to reiterate what they were. So I'll go over the definition of the two again. Authentication is the process of defining an identity and the verification of that identity. For example, a username and password. Authorization, that determines what an identity can access within a system once it's been authenticated. An example of this would be an identity's permissions to access specific AWS resources. As we have already seen, the main service that is responsible for managing and maintaining what an AWS identity is authorized to access is governed by IAM, identity and access management.
So let's start with IAM, and how these permissions are implemented and associated with different identities, allowing the authorization to use specific services and carry out certain functions. When an identity is authenticated to AWS, the way in which permissions are given to the identity varies depending on the identity's own user permissions and its association with other IAM groups and roles.
Let's take a quick recap on users, groups, and roles. IAM users are account objects that allow an individual user to access your AWS environment with a set of credentials. You can issue user accounts to anyone who needs to view or administer objects and resources within your AWS environment. Permission can be applied individually to a user, but the best practice for permission assignments is to add the user to an IAM group.
IAM groups are objects that have permissions assigned to them via policies, allowing the members of the group access to specific resources. Having users assigned to these groups allows for a uniform approach to access management and control.
IAM roles are objects created within IAM, which have policy permissions associated to them. However, instead of just being associated with users as groups are, roles can be assigned to instances at the time of launch. This allows the instance to adopt permissions given by the role without the need to have access keys stored locally on the instance.
Permissions are granted to users, groups, and roles by means of an AWS IAM policy. This policy is in the form of a JSON script. There are a number of pre-written AWS policies, which are classed as AWS managed policies. You can also create your own customer managed policies, too. The AWS managed policies cover a huge range of AWS services at different authorization levels, from read-only to full access. And at the time of this course production, there are currently 218 AWS managed policies in place. If your security requirements fit with one of these AWS managed policies, then that's great and you can start using it right away by associating users, groups, or roles to it. However, it's more than likely that these AWS managed policies are not a perfect match for permissions you want to assign to an authenticated user. In this instance, you can copy and tweak the policy and make it fit for your requirements exactly. When it comes to security, you can't be lazy, as this leads to mistakes and vulnerabilities. You can't afford to take shortcuts, and you need to define your permissions, ensuring they only allow authorized access to services and features that are required.
IAM policies are made up of statements following a set syntax for allowing or denying permissions to an object within AWS. Each policy will have at least one statement with a structure that resembles the following. Statement: this defines the main element of a policy, and groups together the permissions defined within it via the following attributes.
Effect: this will either be set to Allow or Deny. These are explicit. By default, access to your resources are denied, and so therefore if this is set to Allow, it replaces the default Deny. Similarly, if this was configured as Deny, it would override any previous Allow.
Action: this corresponds to API calls to AWS Services that authenticate through IAM. This example represents an API call to delete a bucket, the action with an S3. You are able to list multiple actions if required by using a comma to separate them. Wildcards are also allowed. So for example, you could create an action to carry out all APIs relating to S3.
Resources: this specifies the actual resource you wish the permission to be applied to. AWS uses unique identifiers known as ARNs, Amazon Resource Names, to specify resources. Typically, ARNs follow the following syntax. Let's break this down and take a look at each of these segments. Partition: this relates to the partition that the resource is found in. For standard AWS regions, this section would be AWS. Service: this reflects the specific AWS service. For example, S3 or EC2. Region: this is the region where the resource is located. Now remember, some services do not need a region specified, so this can sometimes be left blank in those circumstances. Account-ID: this is your AWS account ID without hyphens. Again, there are some services that do not need this information, and so it can be left blank. Resource: the value of this field will depend on the AWS service you are using. For example, if I were using the action s3:DeleteBucket, then I could use the bucket name that I wanted the permission to delete, and in this example, cloudacademy is the name of the bucket.
Condition: this element of the IAM policy is an optional element that allows you to specify when the permissions will be activated based upon set conditions. Conditions use key value pairs, and all conditions must be met for the permissions to be activated. For example, there may be a condition only permitting requests from a specific source IP address. A full listing of these conditions can be found here.
Now we have a basic understanding of how JSON scripts are put together and their general flow. Let's see how we can modify existing policies to tweak them to your needs. To copy and edit an existing AWS managed policy is a very simple and easy thing to do, and can save you a lot of time trying to recreate your own if you just need a few small tweaks.
So I'm currently within the AWS management console, at the dashboard of IAM. So from here, you just need to go down to Policies, and then up to Create Policy. Now you can see you got three options here: Copy an AWS Managed Policy, Policy Generator, or Create Your Own. For this demonstration, we want to copy an existing AWS managed policy, and then we can customize it to fit our needs. So we can select that. Now you can filter from this policy list and save you scrolling through the 10s or 100s that there are. So what I'll be inclined to do is to search for roughly what you're looking for. Let's have a look at S3. Let's take a look at the S3ReadOnlyAccess. So select that, and now you can see what the policy looks like. So this is the JSON document, and you can see that it allows the s3:Get and s3:List actions, which will essentially give you read-only access to S3, to any resource. So let's modify this to include an additional permission, for example, CreateBucket. So I can directly edit this policy document and add in our own, so s3:CreateBucket. So now, we have read-only access, and also, we are allowed to create buckets as well.
If we click on Validate Policy, and that will just confirm that the entries we have made are okay, and you see on the top here, it says this policy is valid. If you did edit it, and it wasn't quite correct, then it would let you know. For example, if I removed this comma here and tried Validate Policy again, it would let us know that this policy contains the following JSON error on the specified line, tells you what it expected instead of what it actually has. So if we go back to line 8, add back in our comma, and say Validate, and it can say this policy is valid.
And then from here, all we need to do is give this a new policy name. We can call it S3-Custom-Policy. And then all we need to do is click on Create Policy. And that's it. Now we can verify that that policy exists. We can click on the filter here and say Customer Managed. Because we've edited the AWS managed policy, it now becomes a customer managed policy. And we can see, down here is our policy, S3-Custom-Policy. We can click on it, we can see the JSON document. And that's it.
If you don't feel confident enough to edit existing AWS managed policies, then you could use a tool provided within IAM called the IAM Policy Generator. This allows you to create an IAM policy using a series of dropdown boxes without the need of editing a JSON document itself. The following demo will quickly show you how to access this policy generator and create an example policy.
Okay, so to create a policy using the AWS Policy Generator is, again, very simple, like we've done previously. I'm starting on the screen within IAM, and I'm under Policies at the moment. So from here, all you need to do is click on Create Policy, and again we have the three options, but this time, we want to use the policy generator. So click on Select, and we've got a number of dropdown boxes and options here. So we've got an effect, which we can either have as Allow or Deny. For this example, we're going to have Allow. We then have a list of AWS services. As you can see, there's quite a lot in the list. And we'll select Amazon S3. And now we can pick all the actions associated with S3. If we tick this one here, All Actions, then we get everything, or we can just pick specific permissions. Let's go for Create and DeleteBuckets. And then we have to supply the Amazon Resource Name. So for S3, that will be arn:aws:s3:: and then all resources, Add Statement, and you can see here at the bottom, we have an Allow effect for the s3:Create and DeleteBuckets to all resources within S3, and then we click on Next Step, and we can see here that it's created the JSON policy document for us. So based on those dropdown selections, we now have a full policy document that we can use.
And then we can click on Validate Policy, and as before, you can now see that this policy is valid, so there's no errors in this policy. And now we can give this policy a name. Let's call it S3CreateDelete, and then click on Create Policy. And again, we can have a look and verify that our policy is there by filtering on Customer Managed, and here we have our S3CreateDelete policy, and there you go. That's how you create a policy using the policy generator.
So far, we have covered how to create IAM policies from both an AWS managed perspective and via the policy generator. However, if you are completely at ease writing your own JSON scripts, and want to define their own tight and well-written IAM policies, then you have this option available to you as well. All you need to do is to give your policy a name and a description, and then start writing your permission statements, authorizing any associated identities to access or restrict access to AWS resources. Once you get used to the syntax and benefits of writing your own policies, you'll be able to effectively and efficiently lock down access to your resources to ensure they are only accessed by authorized API calls. There are many, many commands that can be applied and controlled through an IAM policy, but they're a bit beyond the scope of this course. However, AWS does provide great API listings for the different services through their extensive documentation for advanced policy writers.
Let's now take a step away from IAM and move our attention to S3, Simple Storage Service. This is one of AWS' most common storage services, and is used by a multitude of other AWS services. So it's worth devoting some time to see how S3 handles its own authorization. There are multiple ways an identity can be authorized to access an object within S3, which overlap with the IAM mechanisms we have already discussed. So how does a user or service get the correct level of authorization? First, let's define the different methods that permissions can be applied within S3: S3 bucket policies, and S3 ACLs, Access Control Lists.
Bucket policies are similar to IAM policies, in that they allow access to resources via a JSON script. However, as the name implies, these bucket policies are only applied to buckets within S3, whereas IAM policies can be assigned to users, groups, or roles as we previously discussed. In addition, IAM policies can also govern access to any AWS service, not just S3. When a bucket policy is applied, the permissions assigned apply to all objects within that bucket. This policy introduces a new attribute called principles. These principles can be IAM users, federated users, another AWS account, or even other AWS services, and it defines which principles should be allowed or denied access to various S3 resources. Principles are not used within IAM policies as the principle element is defined by who is associated to that policy via the user, group, or role association. As bucket policies are assigned to buckets, we need to have this additional parameter of principles within the policy.
As you can see from this example, a bucket policy is very similar in terms of layout and syntax to that of an IAM policy. However, we do have the Principal attribute added. This value must be the AWS ARN of the principal, and in this example, we can see cloudacademy, as a user within IAM, is allowed to delete objects and put objects within the cloudacademy bucket identified under the resource parameter. S3 bucket policies also allow you to set conditions within the policy, allowing a fine-grain permission set to be defined. For example, you could allow or deny specific IP subnets to access the bucket, or perhaps even restrict a specific IP address. This is another level of access control taking place at the network level that helps to tighten access, ensuring only authorized access is permitted.
I now want to move on to S3 ACLs to show you how these differ. This access mechanism predates IAM, and so is quite an old access control system. S3 ACLs allow identities to access specific objects within buckets; a different layout approach than bucket policies, which are applied at the bucket level only. ACLs allow you to set certain permissions on each individual object within a specific bucket. These ACLs do not follow the same format as the policies defined by IAM and bucket policies. Instead, they are far less granular, and different permissions can be applied depending if you are applying an ACL at the bucket or object level.
The grantee is the resource owner, and is likely to have full control over that object and on new bucket creations. This is typically the AWS account owner. The grantees are defined by the following categories. Everyone: this would allow access to this object by anyone, and that doesn't just mean any AWS user, but anyone with access to the internet if the object is public. Any Authenticated AWS Users: this option will only allow IAM users or other AWS accounts to access the object via assigned requests of authentication. Log Delivery: this allows logs to be written to the bucket when it is being used to store server access logs. Me: this relates to your current IAM AWS user account. From within S3 via the AWS management console, these permissions can be applied via a series of checkboxes, and if all options are selected, then that grantee is considered to be authorized to have full contol of the object. You can have up to 500 grantees on any object.
We have spoken about a number of ways an identity or principal can be authorized access to a resource or object within AWS, but what happens if a principal who belongs to a group and accesses an object in a bucket with S3 ACLs, bucket permissions and their own IAM permissions? Within all of this authorization applied to the principal, how is this access governed if there are conflicting permissions to the object in the bucket that they are trying to access?
Well, AWS handles this permission conflict in accordance with the basis of least-privileged. Essentially, by default, AWS dictates that access is denied to an object, even without an explicit Deny within any policy. To gain access, there has to be an Allow within a policy that the principal is associated to or defined by within a bucket policy or ACL. If there are no Denies defined, but there is an Allow within a policy, then access will be authorized. However, if there is a single Deny associated with a principal to a specific object, then even if an Allow does exist, this explicit Deny will always take precedence, overruling the Allow, and access will not be authorized.
I'd now like to just give a quick demo of how to create S3 ACLs and S3 bucket policies. Okay, for this demo, I'm going to show you how to look at the S3 ACLs and edit those, and also how to create an S3 bucket policy. So I've created a bucket here from within S3 called cademobucket. And looking at the properties of this bucket, if we go down to Permissions, here you'll see the permissions related to the ACL, the access controllers. The grantee is the account owner. So if you wanted to add more permissions to this ACL, we can click on Add More Permissions. Select another grantee. I'll just select Me, and then we can just use the tickboxes to select the permissions that we want, so List and Upload/Delete, and then click on Save. And that'll now give my user List and Upload/Delete permissions to this bucket. And for S3 ACLs, it's as simple as that, really.
So moving on to bucket policies. Let's just delete this. So let's add a bucket policy. Now you can either write your own policy here if you're confident enough, or you can select a sample bucket policy, or use the AWS Policy Generator. So let's go ahead and use the generator. Type of policy will be an S3 bucket policy. The effect we'll have is Allow. So the principal is going to be an AWS user in this demonstration. So if we go ahead and look at our user, the one we created earlier was CAuser1. Here's the ARN of this user, so we shall copy that. And if you notice the permissions that this user's got, it's only read-only access to S3, it's one of the AWS managed policies that was assigned to that user. So we'll put in the ARN of the principal. Service is S3 and the action we will have will be PutObject. And the ARN of the bucket will be arn:aws:s3::cademobucket/ and then any resource. Let's add conditions as well for this. So on the condition of an IpAddress with the SourceIp, being mine, which is 188.8.131.52, so we'll add that condition. We'll add the statement. So here we can see that the principal is the CAuser1, is allowed to put objects within the cademobucket on the condition that the source IpAddress is 184.108.40.206, which is my IP address. Click on Generate Policy. We can then copy that and paste it into our Bucket Policy Editor, click on Save. And that's it, that's the bucket policy applied.
So what I'm going to do now is log out of this account, and log in with the CAuser1 account and try and put an object in that bucket. Okay, so I've logged back in as CAuser1. So I want to try and test that bucket policy now by putting an object within this bucket. So as you can see, I'm within the cademobucket. So if I go to Upload, Add Files, pick a random file, and say Start Upload, and there you can see, the object has been uploaded. So with the use of a bucket policy, I was able to grant additional permissions to this user to allow them to add objects to this bucket, with the inclusion of the conditions as well using the source IP address.
Permissions and authorization can exist at multiple layers within the AWS framework. We have looked at specific user and principal permissions, and how the authorization process is managed.
When we discussed S3 bucket policies, we briefly touched on conditions, and how this can be configured to allow or deny access based on IP addresses, for example.
This network level access control can also be used within your virtual private cloud, VPC, to authorize network traffic in and out of a particular subnet. It's managed differently and offers greater control through the use of network access controllers, or NACLs.
In the beginning of this course, we listed AWS NACLs as an access control mechanism, and indeed they are. However, they provide permission at the network layer. NACLs provide a rule-based security feature for permitting ingress and egress network traffic at the protocol and subnet level. In other words, ACLs monitor and filter traffic moving in and out of your subnet, either allowing or denying access dependent on rule permissions. These NACLs are attached to one or more subnets within your virtual private cloud. If you haven't created a custom NACL, then your subnets will automatically be associated with your VPC's default ACL, and in this instance, the default allows all traffic to flow in and out of the network, as opposed to denying.
The rule set itself is very simple, and has both an inbound and outbound list of rules, and these rules are comprised of just six different fields; these being Rule Number: ACL rules are read in ascending order, and as soon as a network packet is received, it reads each rule in ascending order until a match is found. For this reason, you'll want to carefully sequence your rules with an organized numbering system. I would suggest that you leave a gap of at least 50 between each of your rules to allow you to easily add new rules in sequence later, if it becomes necessary. Type: this dropdown list allows you to select from a list of common protocol types, including SSH, RDP, HTTP, and POP3. You can alternatively specify custom protocols, such as varieties of ICMP. Protocol: based on your choice for type, the protocol option might be grayed out. For custom rules like TCP and UDP, however, you should provide a value. Port Range: if you do create a custom rule, you'll need to specify the port range for the protocol to use. Source: this can be a net or a subnet range, a specific IP address, or even left open to traffic from anywhere. Allow/Deny: each rule must include an action specifying whether to find traffic where we're permitted, to enter or leave the associated subnet or not. So looking at these rules, authorization is permitted or denied by the associated subnet, depending on the verification of the parameters identified in points 2 to 5. This data is analyzed from within the network packet itself. So we are not authorizing a principal here, like we have been looking at with IAM and S3. Instead, we are authorizing the network packet itself.
It's important to note that NACLs are stateless. Therefore, when creating your rules, you'll need to apply an outbound reply rule to permit responses to inbound requests.
I have seen NACLs used very effectively to prevent DDOS, distributed denial of service, attacks. If traffic somehow manages to get past AWS' own DDOS protection undetected, and you're being attacked from a single IP address, you can create a NACL rule that will deny all traffic from that source right at the subnet level, and the traffic will not be authorized to go any further. Just a small point, and this applies to all the authentication and authorization mechanisms I've mentioned thus far: your NACLs will require updating from time to time, and you should regularly review them to ensure they are still optimized for your environment. Security is an ongoing effort and needs regular attention to ensure its effectiveness.
Having the ability to authorize or deny network packets at a network level is great, but can the same be accomplished at an instance level? The answer is yes. Let's see how this level of authorization works. AWS security groups are associated with instances, and provide security at the protocol and port access level, much like NACLs, and as a result, they also work much the same way. Containing a set of rules that filter traffic coming into and out of an EC2 instance. However, unlike NACLs, with security groups, there isn't a Deny action for a rule. Instead, if there isn't a rule that explicitly permits a particular packet, it will simply be dropped. Again, the rule set is made up of two rule sets, inbound and outbound.
But security groups are stateful, meaning you do not need the same rules for both inbound and outbound traffic, unlike, NACLs, which are stateless. Therefore, any rule that allows traffic into an EC2 instance will allow any response to be returned without an explicit rule in the outbound rule set.
Each rule is comprised of four fields: type, protocol, port range, and source. Let's take a look. Type, the dropdown list allows you to select common protocols like SSH, RDP, HTTP. You can also choose custom protocols. Protocol, this is typically grayed out, as it's covered by most type choices. However, if you create a custom rule, you can specify your protocol here. Port Range, this value will also usually be pre-filled, reflecting the default port range or port range for your chosen protocol. However, there might be times when you prefer to use custom ports. Source, this can be a net or subnet range, a specific IP address, or another AWS security group. You can also leave access open to the entire internet using the Anywhere value. We can clearly see here that authorization to the instance can only be permitted if the packet meets conditions within the four parameters. Again, we are not authorizing a principal here, it's the network packet itself. Security groups are a great way to authorize the use of particular ports for communication, whilst restricting all other communication over denied ports.
For example, you could have a number of SQL RDS instances that you want to write to from a group of EC2 instances. In this case, you could create a security group for the SQL RDS instances, and another for the EC2 instances. You will then authorize communication to happen over specified permitted ports, such as 1433 and 1434, used by SQL, between the two groups. All other communication will be dropped and denied, which in turn enhances security on your AWS infrastructure.
That brings us to the end of this lecture on authorization within AWS. Coming up next, we'll look at how we can track and order identities that have been authenticated and are authorized to access specific resources.
Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data center and network infrastructure design, to cloud architecture and implementation.
To date, Stuart has created 90+ courses relating to Cloud reaching over 100,000 students, mostly within the AWS category and with a heavy focus on security and compliance.
Stuart is a member of the AWS Community Builders Program for his contributions towards AWS.
He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.
In January 2016 Stuart was awarded ‘Expert of the Year Award 2015’ from Experts Exchange for his knowledge share within cloud services to the community.
Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages. |
Network traffic classification can be used to identify different applications and protocols that exist in a network. Actions such as monitoring, discovery, control and optimization can be performed by using classified network traffic. The overall goal of network traffic classification is improving the network performance. Once the packets are classified as belonging to a particular application, they are marked. These markings or flags help the router determine appropriate service policies to be applied for those flows. |
Moloch is an open source, large scale IPv4 (IPv6 soon) packet capturing (PCAP), indexing and database system. A simple web interface is provided for PCAP browsing, searching, and exporting. APIs are exposed that allow PCAP data and JSON-formatted session data to be downloaded directly. Simple security is implemented by using HTTPS and HTTP digest password support or by using apache in front. Moloch is not meant to replace IDS engines but instead work along side them to store and index all the network traffic in standard PCAP format, providing fast access. Moloch is built to be deployed across many systems and can scale to handle multiple gigabits/sec of traffic. |
Cyber Security Terminology to Know in 2020
The world is changing almost faster than we can keep up. This is especially true in 2020, the year of COVID-19. Even in a global pandemic—perhaps even more so in a global pandemic when we are relying largely on the internet for communication, connection, shopping, and more—it’s important to stay ahead of the latest terms, concepts, and trends in cyber security. There’s more than one way to keep yourself safe from a virus!
As we conduct our lives over the internet more and more, few people prioritise their cyber safety. Knowing at least a little bit about what’s what on the digital security scene can ensure that your online life continues unheeded by scammers, hackers, phishers, and other cybercriminals. To that end, we have put together a quick guide to the most important cyber security terms in 2020. We have included both basic terms and some new trends in cyber security.
Also called data encryption, this is an important cyber security measure that protects the data of organisations and individuals. When data is encrypted it is scrambled and made unintelligible, and can only be read using an encryption key. This means that even if a cyber attack is successful in obtaining the data, it cannot be used for nefarious purposes.
Social engineering attacks can take many forms including impersonating executive staff and forms of tailgating or convincing staff to gain physical access.
They could also be in the nature of calls to a help desk, or using social interactions to gain staff confidence and familiarity for manipulation at a later date.
Mixing psychology with cybercrime involves attackers using manipulation via email or other messaging. The end goal is to have victims disclose information which can then be
used to steal money or otherwise misused. Often, it’s our human behaviour that leaves us vulnerable. Stay vigilant and never give details to a site or person if you can’t determine their
Malware and anti-malware
Malware is a generic term that refers to any program installed in a computer with the intent to cause harm—corrupt files, damage a system, steal information. You may hear of different types of malware such as ransomware which encrypts a victim’s data with an encryption key known only to the attacker, spyware which gathers information about your browsing habits and sends it to a third party, or trojans which are programs appearing innocuous but serving as a vehicle for some kind of harmful code.
Anti-malware is a broad category of software that is used to combat various kinds of malware attack. It is a great tool in your cyber security arsenal but must be updated regularly to be effective. Expert advice can help to determine which anti-malware products would best suit your needs.
Bots or robots are pieces of software (in this case, malware) that run automated tasks. They are useful for applications such as web crawling or search engines, but can also be put to malicious use to automate attacks.
A botnet is a network of devices running bots, connected over the internet. The owner of the botnet can command the compromised devices and use them to perform various cyber attacks.
Identity fraud is common online. Cybercriminals can invent fake identities of synthetic identities using a mixture of real and fabricated details, often used to open credit accounts and make fraudulent purchases.
Cloud computing and cloud security
Cloud computing and storage is becoming ever more popular for companies and individuals as a way to decentralise their digital storage, reduce dependence on their own hardware, and make it accessible from anywhere. Common applications such as Google Drive, Dropbox, and even Netflix are examples of cloud computing. It is a very helpful tool but can leave data vulnerable to attack.
With the rise of the cloud, cloud security is becoming more and more an essential aspect of cyber security in general. It consists of a range of practices and technologies aimed at deterring cyber threats against cloud users, and cyber security experts like the CANDA team can advise businesses and organisations how best to use cloud security tools to keep their own and their clients’ data and systems safe.
Denial of Service (DOS) attack
A specific type of cyber security threat that has become common is the Denial of Service attack. These effectively shut down the systems of an organisation and shut out legitimate users so they can no longer access things such as emails, websites, and user accounts. This result is achieved by flooding the site with traffic until it cannot respond or crashes entirely. There are many reasons a cybercriminal would want to make a site or system unavailable to its intended users, including monetary or political gain.
A Distributed Denial of Service (DDoS) attack involves multiple devices or sources—often a botnet is used for this.
There are plenty more terms that make up the world of cyber security in 2020 but knowing those explained above will start you on the road to a better understanding. Take a look at the rest of the CANDA blog to learn more about the latest trends in cyber security as well as the best ways to keep yourself and your organisation safe. CANADA also offers cyber security planning and execution services, making it simple for businesses and groups to put together a holistic security program. |
Breaking Down the Pros and Cons of AI in Cybersecurity
The fields of artificial intelligence (AI) and machine learning (ML) are rapidly evolving as new products and techniques are developed. And while much of the discussion surrounding AI is more philosophical in nature—such as ethics and privacy concerns, and what AI means for humanity—the development of real, practical applications marches on.
Within the world of cybersecurity, AI and ML are being used to improve cybersecurity defenses and to launch more effective malware. Cybersecurity companies are using AI and ML to better detect and respond to threats. The power of AI and ML, including subspecialties like deep learning, comes in the ability to rapidly mine large amounts of data, process a huge number of signals, identify anomalies, and develop predictions. Moreover, these systems are continuously learning using new datasets to improve their abilities.
But these same features that make AI and ML useful for protecting systems can also be used by bad actors to identify new vulnerabilities and improve the efficacy of their attacks.
Here are some examples of how both AI and ML are being used for good—and for bad.
Beneficial - Positive Use Cases
Network intrusion detection products use AI to identify anomalies in user behavior or network traffic patterns which signal possible intrusions. They may, for example, analyze a program’s particular sequence of system calls to evaluate whether it is malicious. Or they may look for unauthorized external connections that may have been set up to support an intruder’s command-and-control channel. Or they may flag an unexpected escalation of a user’s privileges. Older systems relied on algorithms which seek certain signatures based on a set of rules, but as the nature of attacks evolve it becomes too difficult to manage this rule base. However, systems that use ML-based algorithms to dynamically augment and adjust its rule base can learn from ongoing patterns of traffic or behaviors to adapt to the changes over time.
Rapid response to a cyberattack is important, and AI and ML techniques may be used in predictive and analytic tools to provide early alerts to potential attacks. Similar anomaly detection approaches used to detect breaches after they occur can be used to also provide alerts to possible impending breaches before they occur by, for example, detecting attempts to scan a network or deliver malware payloads which may be a precursor to an actual intrusion.
Furthermore, AI and ML tools may be used to aid in isolating threats before they can damage systems or to collect forensic data to aid incident response and recovery.
Some video surveillance systems use AI and ML to identify actions that are potential threats, like an object that is left behind that might be an explosive device, or to classify images, such as the color or type of vehicle to aid response.
Botnets are a networked group of computers or devices that can be used to carry out a coordinated assault, such as a denial-of-service attack which floods a victim with an overwhelming amount of traffic. Botnets rely on a command-and-control structure to receive their instructions and to synchronize attacks. One attack mitigation strategy is to disrupt these command-and-control communications. But botnets often use scripted Domain Generation Algorithms (DGAs) to automatically create random addressing to set up the command-and-control structure needed to function—and to quickly restore that function if countermeasures are used to interrupt their communication. Security tools using AI to identify these automatically generated domain names are well suited to rapidly recognize these new domains and shut them down.
Detrimental - Negative Use Cases
Phishing emails are designed to lure victims into following malicious links or providing sensitive information. A person is much more likely to fall victim to a phishing email when it is well-crafted, using personalized information or familiar references. AI can be used to digest and analyze datasets of personal information to automate the process of creating more plausible phishing emails with relatable information.
Bad actors are also looking into methods of attacking AI and ML itself to force it to incorrectly classify data or disrupt the system altogether. Training data is used during the initial development phase for a new ML system, and if a bad actor has access to the system in this phase then the data may be altered or carefully chosen to undermine the system. While people would not typically have system access at this phase, security researchers have demonstrated that when a system is in use, modifications to the data it relies on can cause an error. For example, almost imperceptible modifications to photographic or video images can change how ML systems classify those images.
The malware of the future is already in development, using AI to target vulnerabilities while avoiding detection. Security researchers are working with malware code that can adapt to avoid detection from anti-virus systems. Rather than following a fixed script, this malware can learn from its own experiences to determine what works and what does not. For example, IBM Research developed DeepLocker as a proof of concept and presented it a recent Black Hat conference. DeepLocker is designed to behave as a normal video conferencing system until it recognizes the face of a specific targeted person at which point it launches the WannaCry ransomware on their system.
Coleman Wolf, CPP, CISSP, is a senior security consultant at Environmental Systems Design, Inc. He is also the chairman of the ASIS IT Security Community and a member of the ASIS Security Architecture and Engineering Community Steering Committee. |
What is Security Source Code Review?
Source code review is the practice of reviewing developed code for vulnerabilities. There are many ways to review the security of an application and it is recommended to perform more than one method to help ensure more assessment coverage. Penetration testing is great at finding certain bugs such as technical signature or API based issues. Issues related to privacy, information leakage, denial of service are more suited to code review. Source code review is also good practice as you are finding issues early in the SDLC. Locating and fixing issues early in your SDLC makes it cheaper in terms of effort and cost to remediate. It also empowers developers to understand security bugs at the source code level such that they may not repeat the same mistakes.
What is static analysis?
Static Code Analysis is usually performed as part of a Source code review and is carried out at the Implementation phase of SDLC. Static Code Analysis commonly refers to the running of static code analysis tools that attempts to highlight possible vulnerabilities whiting the ‘static’ (non-running) source code by using techniques such as Taint Analysis, Data Flow Analysis, Control Flow Graph, and Lexical Analysis. When the analysis is performed on a runtime environment, it is referred to as Dynamic Code Analysis. Ideally, such tools would automatically find security flaws with a high degree of confidence that what is found is indeed a flaw. However, this is beyond the state of the art for many types of application security flaws. Thus, such tools frequently serve as aids for an analyst to help them zero in on security relevant portions of code so they can find flaws more efficiently, rather than a tool that simply finds flaws automatically. |
UEBA (User & Entity Behavior Analytics) is the most promising solution to fight against cyber threats and fraud as it allows us to get ahead of the attackers by detecting risks and restrict them.
UEBA successfully detects malicious and abusive activity that otherwise goes unnoticed, and effectively consolidates and prioritizes security alerts sent from other systems. Organizations need to develop or acquire statistical analysis and machine learning capabilities to incorporate into their security monitoring platforms or services. Rule-based detection technology alone is unable to keep pace with the increasingly complex demands of threat and breach detection.
PAE uses UEBA to provide insights on cyber security and analytics. Our solution analyses volumes of data to establish a baseline of normal user and system behavior, and flag suspicious behavior anomalies. The result is a sophisticated artificial intelligence platform that detects insider and cyber threats in real time.
ProActeye can automatically correlate IP addresses with associated MAC address, device profile, location data and associated employee identity. This will help the organization to save lot of time spent on finding these details. It is capable to disable access of such application from source IP address which will act as a prevention measure on immediate basis. It is capable ro disable access based on role on the NAC.
PAE is capable to associate VPN source IP address with associate MAC address, device profile, location, User identity and their role. This will help to monitor all activities done by VPN user and detect any abnormal activity. This is capable to disable access to such users.
This is capable to generate Email and http alerts as well for such incidents.
The system provides trend of events happening over a period of time which would help the system analyst to understand the behavior of such events and can predict the trends of such occurrence. This would prove very helpful in finding or investigating critical system issues. |
Reverse Engineering Mac Malware 4 - File Analysis
Methods and tools for Mac file analysis, including Dtrace, fs_usage and fseventer, are extensively analyzed by Sarah Edwards in this part of the presentation.
Reverse Engineering Mac Malware 3 - Dynamic Analysis
The issues described and analyzed in this part are all about dynamic analysis of Mac apps, including virtualization, application tracing and applicable tools.
Reverse Engineering Mac Malware 2 - Mach-O Binaries
Sarah Edwards provides an extensive review of Mach-O binaries, including the types thereof, file signatures, and tools applicable to reverse engineer them.
Reverse Engineering Mac Malware
Digital forensic analyst Sarah Edwards presents an extensive review of tools and approaches applicable for reverse engineering Mac malware at B Sides event.
A Mac OS X Rootkit Uses the Tricks You Haven’t Known Yet 4 - Integrity Checkup with System Virginity Verifier
At the end of their talk, TT and Nanika outline a method to gain root permission on Mac OS X and present their tool called System Virginity Verifier (SVV-X).
A Mac OS X Rootkit Uses the Tricks You Haven’t Known Yet 3 - Benefits of the Host Privilege
Moving on with their presentation, the Team T5 experts delve into host privilege on Mac OS X in terms of the scope of permissions that a normal user can get. |
Web server: nginx
I am looking for a technique to auto block an IP address when for example an attacker IP makes more than 100 requests per minute.
The above article mentions about rate limiting per user based on number of requests and connections. This question is not about rate limiting but denying the IP.
There is a section about Denylisting IP Addresses in the above article - it says:
If you can identify the client IP addresses being used for an attack, you can denylist them with the deny directive so that NGINX and NGINX Plus do not accept their connections or requests.
I believe this is a manual process to observe the IP address used for an attack and then add them to the deny list. Is there a simple way to automate this? |
Do you know what ZeroCrypt Ransomware is?
Our cyber security specialists have recently tested a program called ZeroCrypt Ransomware. Evidently, it is a ransomware-type application whose primary objective is to encrypt the files stored on your computer and then demand that you send its developer money to get the decryption software/key. You should not comply with the request to pay and remove it instead because there is no telling whether this ransomware’s developer will send you the promised decrypter. For more information on this new computer infection, we invite you to read this short description.
At the time of this description, however, ZeroCrypt Ransomware’s dissemination methods are unknown. Nevertheless, we would like to discuss some of the more likely methods that can be used to distribute it. Email spam is a ransomware developer favorite. The email contains an attached file that can feature the main executable or a dropper file that connects to the C&C server and downloads the main executable. Nevertheless, the email can also feature a link that will download this ransomware once clicked. Alternatively, it could be distributed using exploit kits featured on infected websites. Exploit kits such as the Angler Exploit kit interact with a browser’s Java and Flash browser add-ons and secretly download the ransomware when you interact with Java or Flash-based content featured on the infected website.
The sample our security experts have tested created a folder named ZeroCrypt in %LOCALAPPDATA% and placed its randomly named executable file. It also created a Point of Execution (PoE) at HKCU\Software\Microsoft\Windows\CurrentVersion\Run, a string named ZeroCryp of which the value data features the %LOCALAPPDATA%\ZeroCrypt file path. Once the executable and PoE were in place, ZeroCrypt Ransomware began encrypting files.ZeroCrypt Ransomware screenshot
Scroll down for full removal instructions
Our security experts found that ZeroCrypt Ransomware uses the RSA-1024 encryption algorithm. Hence, this ransomware is set to encrypt files using a 1024-bit length key. Testing has shown that it is designed to indiscriminately encrypt almost all files in all locations on your computer. However, we have found that it skips the most vital operating system files in %WINDIR%, but this location is not excluded from the encryption process as some files in it are set to be encrypted. When this ransomware encrypts files, it also appends them with the .zn2016 file extension. Furthermore, it will create a file named ZEROCRYPT_RECOVER_INFO.txt in each folder where a file was encrypted. This particular file is the ransom note that contains information on what you are supposed to do once your files have been encrypted.
The ransom note says that in order to get the decryption key to decrypt your files, you need to send 10 BTC to the provided Bitcoin wallet. 10 BTC is an approximate 7243.95 USD which is a staggering sum of money. Nevertheless, it gets better because, in order to receive the decryption program in which you have to enter the expensive key, you need to pay 100 BTC which is 72,439.38. Now, this might be some sort of mistake because one in their right mind would risk paying either one of these sums because no file is that important or valuable and there is no guarantee that you will receive the decryption key and software.
In conclusion, ZeroCrypt Ransomware is a dangerous piece of software and can encrypt your personal files using an advanced encryption algorithm. At present, there is no way to decrypt its encryption key for free, so this ransomware is extremely dangerous. Its developers want you to give them money in exchange of the decryption key and software, but there is no guarantee that you will receive it. Therefore, you cannot trust its developers, and since there is no apparent way out of this situation, we recommend that you remove this ransomware using the guide below or SpyHunter, a powerful antimalware application that will delete this infection without difficulty.
Delete this ransowmare’s files
- Simultaneously hold down Windows+E keys.
- Enter %LOCALAPPDATA% in the address box and hit Enter.
- Find the folder named ZeroCrypt and Delete it.
- Close the File Explorer window.
- Then simultaneously hold down Windows+R keys.
- Enter regedit in the box and hit Enter.
- Find the registry string ZeroCrypt and delete it.
In non-techie terms:
ZeroCrypt Ransomware is a simple and yet dangerous ransomware-type infection that is secretly distributed using an unknown channel. If it enters a computer, it encrypts most of the files on it and then shows a ransom note that demands an unreasonable sum of money. Also, there is no telling whether the developers will give you the decryption program and key once you have paid. Therefore, you ought to delete this infection as soon as you can using our guide or SpyHunter — our recommended antimalware application. |
The use of decoys as a security measure in organisations is known as honeypotting. It is through “honeypots” that this objective is achieved, by designing and deploying synthetic services attractive to attackers who, in the event of compromising the perimeter, these services will become their potential first target.
This achieves two goals: the first is to stop the threat, because instead of engaging a real environment, it is focused on a synthetic decoy. And secondly, and duly treating the honeypot, to learn more about the adversary and their techniques and/or intentions, through the analysis of the evidence that these adversaries leave on the decoys.
The use of honeypots is currently widespread in organisations, although it should be noted that it is the organisations with a higher degree of maturity in cybersecurity that are adapting them and taking full advantage of them. |
Kops needs an S3 bucket that stores the configuration and status. In addition, use Route 53 to register the Kubernetes API server name, and etcd server name to the domain name system. Therefore, use S3 bucket and use the Route 53 that we've created in the previous section.
Kops supports a variety of configurations, such as deploying to public subnets, private subnets, using different types and number of EC2 instances, high availability, and overlaying networks. Let's configure Kubernetes with a similar configuration of network in the previous section as follows: |
Gmail Pastebin. Continue reading. Amongst the servers attacked were DOTA2, LOL, Battle. Anonymous announced Saturday that DDoS attacks on the Muslim Brotherhood will continue until November 18. Operation Blackout's plan to shut down the internet this weekend may have come to nothing, but there is a way in which DNS servers can be used as part of a malicious attack. Just to help anyone who may be stuck with a bricked Shield: 1) Get a. For the second time in a week, Pastebin. WordPress – and anything created with PHP – is dynamic website, which means that each time someone view your site, it has to be build from ground up. DDoS variant. Root Cause Analysis – Datacenter Connectivity Issues Sept 15-16 2015 On the morning of September 15, 2015 connectivity to our Long Island datacenter was interrupted by a distributed denial of service (DDoS) attack against a neighboring subnet in the datacenter. WordPress tips for protecting against DDoS attacks. 破解 编程 代码 路由器 密码 wifi 攻击 渗透 黑客电影 wireshark 抓包 隐私窃取 Kali 谷歌 查资料 防火墙 google avast 杀毒软件 许可文件 黑客 XSS apt 钓鱼 脚本 shell 黑客工具 分享 安卓软件 网络安全 SQL VPNgate Youtube VPN Linux 母亲 自己 人生 USB攻击 Ubuntu Metasploit Python JS. While we have previously encountered huge distributed denial of service (DDoS) attacks that appear to come from nowhere and flood the victim's network security, we have begun to see much more stealth and more sophisticated attacks causing just as much, if not more, damage. WordPress Sites Exploited Through Brute Force: 3 Simple Ways to Protect. Estatísticas do Pastebin para V8SVyu2P. The FortiGuard Labs threat r. These infected PCs are collected and controlled in the form of "Botnets," and can be used to launch coordinated Distributed Denial of Service attacks (DDoS) and other cyber-attacks. onion urls Introduction Points Torbook – Torbook – The Tor social Network, Get in contact with others!. As an individual WordPress administrator you do not have the resources and infrastructure to fend off a DDoS attack. Attack vectors observed include: • Volumetric DNS DDoS • Volumetric Layer 3/4 DDoS • Volumetric Layer 5-7 DDoS. Bonjour, Tout d’abord un grand merci pour votre site car je suis novice et j’y récupère beaucoup de précieuses informations / astuces pour mon site, et vous rendez le sujet encore plus passionnant qu’il ne l’est déjà !. You may have heard of an anonymous publishing website called PasteBin. To reduce the effect of the attack we decided to block Motorola IPSC connection from DMRNET’s network till further notice. Author Izz ad-Din al-Qassam Cyber Fighters posted its latest threat on Pastebin, again claiming the attacks are in retaliation for the portrayal of. 10 WordPress Security Tips for Advanced Users. saya tidak bertanggung jawab jika terjadi hal yang tidak di inginkan, :d. The official https:// pastebin. While we have previously encountered huge distributed denial of service (DDoS) attacks that appear to come from nowhere and flood the victim’s network security, we have begun to see much more stealth and more sophisticated attacks causing just as much, if not more, damage. These affected sites like WordPress or Spamhaus and even led to the destabilization of the virtual currency Bitcoin. Description: Web page contains spammy keywords specific to various black hat SEO campaigns (pharma spam, porn, replicas of popular brands, payday loans, etc. Search the history of over 380 billion web pages on the Internet. 롤백을 하게 된 이유는, XRF071과 XRF082가 전체 모듈 26개가 인터링크 되어 있는 상태에서, 한 모듈에서 교신을 하는 도중 다른 모듈에서 교신이 있게 되면, 같은 리플렉터 내에서는 신호가 전달이 되지만, 반대편. Security experts from Radware have spotted a new botnet dubbed DemonBot that it targeting Hadoop clusters to launch DDoS attacks against third parties. How to identify, block, mitigate and leverage these xmlrpc. /HACKING_BACKTRACK. 2017-05-28 青楚 阅读(912) 评论(0) 据外媒 27 日报道，Recorded Future 安全专家发现一名德国黑客通过 Pastebin 网站传播 Houdini 蠕虫。 调查显示，Houdini 蠕虫开发人员似乎也是开源勒索软件 MoWare HFD 变种的创建者之一。安全. com Name: Matthew Williams Most used password: matthew23 Other used password: udp Location: Georgia 30058 Welcome to Chatango!. have announced availability of the Prolexic Q2 201 4 Global DDoS Attack Report. Adding $20 it is possible to power massive DDoS attacks that can peak 290 and 300 Gbps. This is not something in my control. Net Reflector, but now I like ILSpy. How to identify, block, mitigate and leverage these xmlrpc. Attackers uses pastebin. Pastebin shut down twice in a week by DDoS attacks Details Created on Friday, 06 January 2012 21:48 Pastebin. These infected PCs are collected and controlled in the form of "Botnets," and can be used to launch coordinated Distributed Denial of Service attacks (DDoS) and other cyber-attacks. Passwords, and #OpKKK Nowadays, researchers, hackers, and the media bombard us with tons of information security (InfoSec) news each week. It provides a central place for hard to find web-scattered definitions on DDoS attacks. On December 2, an offshoot of LulzSec calling itself LulzSec Portugal attacked several sites related to the government of Portugal. CLDAP Protocol Allows DDoS Attacks with 70x Amplification Factor In a report released on Tuesday, Akamai says it spotted DDoS attacks leveraging the CLDAP protocol for the first time, and attacks using this protocol have the potential to incur serious damage, based on the opinion of its experts. 218 - - [01/Dec/2013:04:24:14 +0100] "GET / HTTP/1. The al-Qassam Cyber Fighters Tuesday announced via Pastebin the fifth week in what it often by using vulnerabilities related to WordPress or. Pada tingkat yang paling mendasar, yang Distributed Denial of Service (DDoS) serangan menguasai sistem target dengan data, seperti bahwa respon dari sistem target baik diperlambat atau dihentikan sama sekali. 1 post published by operationgreenrights during June 2013 Operation Green Rights We fight for human rights and against industries which destroy nature and ancient cultures. Pastebin shut down twice in a week by DDoS attacks Details Created on Friday, 06 January 2012 21:48 Pastebin. According to researcher Denis Sinegubko, Pastebin was used as a remote server for malcode. Hacktivists, extortionists and blackmailers frequently use DDoS attacks. Top websites screwed over in WordPress. com, GitHub. and web store for each and every occasion. php scans, brute-force, and user enumeration attacks on WordPress sites… Secure WordPress xmlprc. The image bellow, originally posted on 4chan. com; verifying pingback from 185. Nulled is a cracking community, we already have tons of cracked/nulled tools to offer. 174', '2016-08-08 23:39:20', 'null', 'nu', 'WordPress/4. com, estare esperando tu mensaje un abrazo. so far my fixes have not been overwritten. ElevenPaths’ analyst team presents the case of the Delta-Stresser. Morgan Chase today. Of course, this statistic doesn’t directly correspond to the number of visits to infected pages. com hack exposes confidential code by Dan Goodin, theregister. based banks, Operation Ababil. The Spamhaus DDoS attack was so great that it affected the speed of the Internet globally. A Distributed Denial of Service (DDoS) can be launched from anywhere and could bring down not only companies but entire countries as well. CISC 250 final project Sources: Norse Map clip: https://www. com/watch?v=TgeTX5ppPJw Denied Image: https://yeupsac. Over the past few years, several major distributed denial-of-service (“DDoS”) attacks took place, including a major event affecting the domain name service provider Dyn, which caused outages and slowness for a number of popular sites, including Amazon, Netflix, Reddit, SoundCloud, Spotify, and Twitter. DDoSPedia is a glossary that focuses on network and application security terms with many distributed denial-of-service (DDoS)-related definitions. The first contact of Blaue reiter and Reflector happened during the Mini World Change Noodles Is Gone in January 2017. c You can get the source code to compile it here: http://pastebin. Distributed Denial of Service (DDoS) attacks typically target websites in an attempt to bring down or ‘crash’ the site. Anonymous successfully performed DDoS attacks on eight Tunisian government websites. Here you can check, if your WordPress domain participated in the DDoS. DoctorBass identifies himself with Anonymous Australia and has been leaking databases since February 2012. The official https:// pastebin. com, written in C, multi. These infected PCs are collected and controlled in the form of "Botnets," and can be used to launch coordinated Distributed Denial of Service attacks (DDoS) and other cyber-attacks. However, they have denied any such political affiliation. Net Reflector, but now I like ILSpy. com - Page Removed" and the rest of the content was identical to what Pastebin. The only thing that isn't is the Wordpress updater. Riecco gli Anonymous che, dopo tanto tempo, tornano con una forte azione simbolica, ovvero il down di ieri del sito del Vaticano. This is not the first time a CMS, and in particular WordPress, has been used for DDoS or other malicious activity. Nearly all our servers are behind a pfSense router. Pastebin Hit by DDoS, Again. CentOS is available in Cloud Server Linux. The Anonymous Team Have Also Developed their own DDoS tool which is said to exploit SQL vulnerabilities to support the group's future campaigns. • Present an overview of reflector and amplifier attacks. Tal compañía fue saturada a través de un ataque de denegación de servicio, aquél usuario famoso sufrió un DDoS, etc. Emergency Windows Patch, Malware Vs. Why did they do it?. Update: this entry is now also a guest post over at my colleague Brett Hardin’s Miscellaneous Security blog. pretty juvenile, script kiddie stuff. ABOUT This site is dedicated to providing the latest coverage on European Cyber Army's Operations, Hacks, and Attacks The European Cyber Army is a collective of hackers who dedicate themselves to providing a voice for the voiceless. This banner text can have markup. org Apache Subversion (SVN). Sebagai anak pertama, beban dan tanggung jawab yang saya saya miliki sangat besar. عرض ملف Faseela Ashraf الشخصي على LinkedIn، أكبر شبكة للمحترفين في العالم. It's suppose to take websites offline in one try, if XML-RPC is activated by the Administrator at /xmlrpc. October 27th, 2016. Interestingly, attackers did not use any botnet network, instead weaponized misconfigured Memcached servers to amplify the DDoS attack. “This attack was the largest attack seen to date by Akamai, more than twice the size of the September 2016 attacks that announced the Mirai botnet and possibly the largest DDoS attack publicly disclosed,” said Akamai, a cloud computing company that helped Github to survive the attack. For the past 14 days I have had my website being hit by millions of WordPress installs over the world which. 28 on Kamis, 17 April 2014. Find out what you need to know about the attack and what you can do to secure your devices in this short slide. #!sunnydays. Volumetric distributed denial-of-service (DDoS) activity peaked at approximately 300 Gbps/24 Mpps for UDP floods and roughly 35 Gbps/91 Mpps for TCP, according to the Q2 2014 report by Verisign. A VPS (Virtual Private Server) offers you the sweet spot between shared web hosting (free) and dedicated hosting. The enemy of my enemy is my friend, right? Victims of the various cyber-attacks by members of the hacktivist group Anonymous are undoubtedly enjoying a bit of schadenfreude this weekend, as a new report from Symantec indicates that some Anonymous members have been tricked into downloading and running a fairly unpleasant Trojan alongside one of their distributed denial-of-service tools. Addressing distributed denial-of-service (DDoS) attacks designed to knock Web services offline and security concerns introduced by the so-called “Internet of Things” (IoT) should be top cybersecurity priorities for the 45th President of the United States, according to a newly released blue-ribbon report commissioned by President Obama. 182 - - [23/Sep/2013:17:28:25 +0200] "GET / HTTP/1. On 30th January 2016 someone started carrying a series of powerful Distributed Denial-of-Service attack (DDoS) on Pastebin. In 2002, service disruption was reported at 9 of 13 DNS root servers due to DNS backbone DDoS attacks. sebenernya sih udah lama, tapi baru ane sempetin kali ini :p pernah ane jalanin di localhost trus coba ane ddos web,. Text files containing emails, passwords and other. Do not scan any devices that you do not have explicit permission to scan. 요즘 memcached 서버 DOS 취약점으로 잠깐 시끌벅적했습니다. In November 2017, a group of researchers provided a macroscopic characterization of the DoS ecosystem; they shared their findings at the AMC Internet Measurement Conference in London. 1" 503 913 "-" "WordPress/3. Neither are making any progress. WordPress contributor Sybre Waaijer identified the security issue and confidentially disclosed it to the WordPress plugins team. The fact is that attacks evolve. Returned home from a vacation, you just wanted to copy the beautiful photos into your computer. A typical WordPress page will try to load dozens of static resources from this URL. I'm looking for an open-source pastebin web-application written in either Python or Perl. Cloudflare keeps your websites and web applications secure — even against the largest of DDoS attacks. DDOS TOOL LIST FROM ANONYMOUS Diposting oleh Unknown di 13. Система хранения данных — ssd или nvme на выбор. have announced availability of the Prolexic Q2 201 4 Global DDoS Attack Report. Stack Exchange Network. Nearly all our servers are behind a pfSense router. WordPress xmlprc. In just 13 minutes it made 181,301 connections. Hey guys! Mahmoud from LetBox just sent over an interesting deal, and we’re happy to feature this brand once again, it’s always been a popular one. Is it possible to proactively stop threats that would otherwise make it past your infrastructure?. Anonymous announced Saturday that DDoS attacks on the Muslim Brotherhood will continue until November 18. WordPress Sites Exploited Through Brute Force: 3 Simple Ways to Protect. With a multitude of rich feature—-including support for over 75+ programming languages and autocomplete capabilities—you'll be able to easily write, edit, and collaborate on your code from any device. This week's sponsor: Recorded Future. [Total: 0 Average: 0/5] The hackers are attacking available on the Internet Elasticsearch clusters with the goal of turning them into DDoS botnets. Can it protect against any DDoS attack? This plugin protects against DDoS CAUSED by brute-force attacks ONLY. After getting pounded with ransomware and malware for deploying distributed denial-of-service (DDoS) attacks, unpatched Confluence servers are now compromised to mine for cryptocurrency. ElevenPaths’ intelligence analysts team reveal the details of in this research report. Using a cache plugin for a WordPress website is in my opinion a required action, but for this website was this not enough and I decided to “hide” my online property behind a proxy or firewall. On Tuesday, the site tweeted that it was under DDoS attack. UDP based DDOS reflection attacks are a common problem that network defenders come up against. com keyword after analyzing the system lists the list of keywords related and the list of websites with related content, in addition you can see which keywords most interested customers on the this website. Text files containing emails, passwords and other. Please email [email protected] DoctorBass identifies himself with Anonymous Australia and has been leaking databases since February 2012. A DDoS using SSDP is an Internal Network Attack. 德国黑客利用 Pastebin 网站传播 Houdini 蠕虫病毒. Even wiki isnt. Conexão – Usando pastebin. A typical WordPress page will try to load dozens of static resources from this URL. If you're attacked by Anonymous the world is going to know because the announcement will be on Pastebin in 24 hours, whereas if you're attacked by cybercriminals, people might never find out, he said. Notice - the source port for the response is not 1900 (but the dst port is okay). The attack utilized a new reflection attack vector based on the exposure of the Memcached Unix service, which was known to have security vulnerabilities, to the internet. The WordPress sites used in the attack are called reflectors. Do not scan any devices that you do not have explicit permission to scan. Headline incidents are only part of the story, however. With no recourse, "they" can create a million Pastebin accounts on the fly and go to town… So with respect I'm not really sure this, "You really shouldn't post your code in Pastebin" would have any real world impact. An attack is defined as a large flood of packets identified by a tuple: (Protocol, Source Port, Target IP). I have found ILSpy to decompile more accurately than JustDecompile (which is still in Beta). Joomla en Wordpress. purpose you can download the source code of that tool from pastebin. 25 Gbps RIPv1 reflection DDoS attack. Sucuri reports on a denial-of-service attack that used thousands of legit WordPress sites to distribute the attack by sending fake pingbacks “from. com/raw/XWFfm5hh Central Com. A post to text board Pastebin associated with the message states that “We will fight always and everywhere the enemies of freedom of speech…Freedom of speech and opinion is a non-negotiable. CYBERCRIME DOS & DDOS ATTACK ETIKA PROFESI TEKNOLOGI INFORMASI DAN KOMUNIKASI Diajukan untuk memenuhi presentasi progam diploma III Disusun oleh : Margaretha (18112104) Arina Putri (18114192) Yanti (18114193) Desy Y. Full details of the command and the background can be found on the Sans Institute Blog where it was first posted. Posts about occupy-wall-street written by NetSecurityIT. Halo gengs dimalam minggu ini gw mau share tutorial deface website dengan auto exploit magento gak usah banyak bacot langsung saja ke initin. i was recently hired to update a website that was affected by this attack. I found my site attacked by ‘neomann’ 2 days ago,hosted on godaddy. On February 28, 2018, GitHub website was hit with the largest-ever distributed denial of service (DDoS) attack that peaked at record 1. Traditionally, DDoS attacks have made use of workstations or routers infected with malware. org, one of the top 1000 most visited sites in the United States and the world!. Prolexic Technologies, now part of Akamai, is a recognised leader in Distributed Denial of Service (DDoS) protection services, and has produced the quarterly Global DDoS Attack Report since 201 I. Make a Donation. htm files has been spotted everywhere. A linkback is a method for Web authors to obtain notifications when other authors link to one of their documents. On 20th February 2014 the Niklaus Wirth Birthday Symposium took place in Zurich. "The Soca website is a source of information for the general public which is hosted by an external provider. The new kid on this attack block is NTP. 5 billion requests a day subject to DDoS attacks and its content isDNS reflector attack. CYBERCRIME DOS & DDOS ATTACK ETIKA PROFESI TEKNOLOGI INFORMASI DAN KOMUNIKASI Diajukan untuk memenuhi presentasi progam diploma III Disusun oleh : Margaretha (18112104) Arina Putri (18114192) Yanti (18114193) Desy Y. com and several others Command line output Useful for integration into other tool outputs Pastelert Tracks keyword searches against pastbin. – DDoS Attacks – DDoS attacks are nothing new, but recently, attackers have started utilizing a new-old approach in the form of reflection attacks. The pdf document is now available on this link on this wordpress blog. In 2002, service disruption was reported at 9 of 13 DNS root servers due to DNS backbone DDoS attacks. How CloudFlare client-side DDOS detection works I was looking through Hacker News today, and upon clicking one of the links a screen popped up, pictured below: This is interesting. During Q1 2015, the gaming sector was once again hit with more DDoS attacks than any other industry. When the stress testing industry was still new, it consisted of raw UDP stress tests and regular GET HTTP requests. , the global leader in content delivery network (CDN) services, today announced the availability of the Q3 2015 State of the Internet - Security Report. gov, the public website of the U. Contribute to pandazheng/SecuritySite development by creating an account on GitHub. This technique is used to avoid malware to be easily spotted, since big encoded chunks of code or other unusual functions can trigger the simplest of the tools. It appears that 4chan users, specifically residents of the /b/ section, are fed up with their content being re-published and shared on 9gag. Daha önce dışarıdan wordpress e api ile müdahale eden var mı elinde bununla ilgili bi örnek olan? Kendi dökümantasyonunda her şey parça parça bide herşeyin bir alternatifi var iyice kafa karıştırıcı olmuş dışarıdan yetkiyle beraber post gönderebileceğim bi örnek olan var mı elinde?. Al continuar usando este sitio, estás de acuerdo con su uso. Well i did remove it but no luck i have feeling its related to Wordpress in version 2. Distributed Denial of Service (DDoS) attacks occur when multiple computers target one system and flood it with so much incoming traffic that the targeted system cannot be used. For all the pasts it finds it scans the raw contents against a series of Yara rules looking for information that can be used by an organisation or a researcher. A linkback is a method for Web authors to obtain notifications when other authors link to one of their documents. Ok, so i was just logging in have a quick snoopof everyone's twitter etc WHEN I FIND Tweets from @NetBashers Calling us Skids and how we can't DoX Them, well considering i did 10 MINUTES, of a little digging thanks to my friends at google here ya go:. Master Nmap quickly with this cheat sheet of common and not so common options. 182 - - [23/Sep/2013:17:28:25 +0200] "GET / HTTP/1. First in our series of WordPress videos specifically for creative and non-technical folks. Update: this entry is now also a guest post over at my colleague Brett Hardin’s Miscellaneous Security blog. Also WordPress Duplicator reminds users to remove the leftover files from their Duplicator migration. The hive server is intended to coordinate DDoS attacks so as to maximize their effect. They serve over 12 million websites, have successfully headed off some intense DDoS attacks, and until recently with the 8ch deplatforming, were quite infamous with the MSM and leftists in general for providing their services to le ebil nahzees, which included The Daily Stormer up until 2017. wordpress_plugin_security_testing_cheat_sheet - WordPress Plugin Security Testing Cheat Sheet. Scribd is the world's largest social reading and publishing site. Joomla and other PHP-based applications were also compromised. En este artículo vamos a ver como eliminar los “meta-datos” de una imagen. + [01/2018] - UFONet (v1. To reduce the effect of the attack we decided to block Motorola IPSC connection from DMRNET’s network till further notice. com; 2011 Pastebin: ¿Cómo un sitio para compartir código populares se convirtió en el lugar de reunión de hackers final (incluyendo el primer gran Sony hackear) 2012 Pastebin: Correr el sitio donde los hackers difunden sus ataques. Dalam rangka untuk menciptakan jumlah lalu lintas yang diperlukan, jaringan komputer bot zombie atau yang paling sering digunakan. As with any DoS attack, the objective is to make a target unavailable by overloading it in some way. It is a quicky, and since the bad guys is also monitoring us now I'll make it short. Our network in Romania is not affected because our ddos protection can filter these size of attack. Entradas sobre ddos escritas por lekee. You leave him, block his Facebook account, and update the name on your profile to hide your. With the help of the Pastebin statistics, we can see that this script has already been loaded more than 2 million times. Continue to report ISIS twitter accounts, but do not DDoS them. Since these guys have been posted quite a few times, I’m going to skip the formalities and just get right to the point. have announced availability of the Prolexic Q2 201 4 Global DDoS Attack Report. Stack Exchange Network. The attacks used various attack techniques to cause site availability and performance disruptions. SNMP DDoS Attacks Spike "These actions will lead to a flood of SNMP GetResponse data sent from the reflectors to the target. Too many techs. com放置恶意软件。. This year organizations are estimated to have spent more than $124 billion on security, yet phishing attacks continue to bypass email security technology. com suffered from a distributed denial-of-service (DDoS) attack. I mean "PerL DDos Script (Save As : ". Next time please pastebin them please?. ddos,ddos attack,how to ddos,dos,what is ddos,ddos attacks,dos vs ddos,ddos attack explained,what is a ddos,ddos protection,d-dos,what is a ddos attack,rainbow six siege ddos,denial of service,ddos wifi,fail ddos,stop ddos,atque ddos,pengu ddos,distributed denial of service,ddos tester,twitch ddos,ddos github,ddos defense,attaque ddos,ddos program,stopping ddos,ddos a website,kids cant ddos. In computing, a denial-of-service (DoS) or distributed denial-of-service (DDoS) attack is an attempt to make a machine or network resource unavailable to its intended users. Uma típica página do WordPress tentará carregar dezenas de recursos estáticos dessa URL. Continue reading “PROTONMAIL UNDER DDOS” →. Just now they have leaked 3 bits of this data which comes as a very partial leak compared to the main data they are said to have. Onderzoek naar pastebin. edu/~amb943/wordpress". Called "Ghost-P2P", the platform will incorporate a "target voting" feature that will enable participants to vote on DDoS targets to be attacked using LOIC, an open source network stress-testing application. World of Tanks NA apparently. In the last event, the cryptocurrency suffered a 25% loss. OpIsrael has been going hard out leaking data from israel sites, wiping databases and leaving sites defaced or inaccessible from ddos attacks. Howto detect malware’s with WP-CLI; Malware plugin’s to WordPress (woocomerce & aksimet) Malware Scanner Tools for Linux; wp-crawl. Distributed Denial of Service attacks were common in the last months. A dissatisfied customer has breached the server of TrueStresser, a DDoS-for-hire service, pilfered its database, and leaked some of the content online. How can I detect a DDoS attack using pfSense so I can tell my ISP who to block? I don't want to block the attack myself, I just want to get alerts / be able to view a list of IP addresses that are using way more bandwidth than normal. tv, Posts Everything Online [Updated] Anonymous also noted that there is a Twitter account for Mr. Let us know your favorite in the comments section below! The post 4 Alternatives That May Be Better Than Pastebin appeared first on MakeUseOf. The infamous organization launched DDos attacks against and defaced more than 500 Chinese sites, leaving the following message on the government homepages: “Dear Chinese government, you are not infallible, today websites are hacked, tomorrow it will be your vile regime that will fall. The attack leverages on a CLDAP zero-day vulnerability, a similar attack has been observed last week, and experts believe that could become another option in the arsenal of hackers in the wild. In September of 2012, U. WordPress "Pingback" DDoS Attacks, (Wed, Mar 12th) Posted by admin-csnv on March 12, 2014. A Distributed Denial of Service (DDoS) is a type of Denial of Service (DoS) attack in which the attack comes from multiple hosts as opposed to one, making them very difficult to block. Getting yourselves prepared for the worst is the first line of defense, especially if your organization is a stakeholder in one of these events. This is not the first time a CMS, and in particular WordPress, has been used for DDoS or other malicious activity. ElevenPaths’ intelligence analysts team reveal the details of in this research report. To a very large extent, this is because WordPress appeals to users that do not have the resources to manage their websites and they often use WordPress to make their job easier. Last week the al-Qassam Cyber-Fighters (AQCF) worked said on PasteBin that they were going to start on their 55th day of their distributed denial of service (DDoS) campaign against large U. When Red Gate said there would no longer be a free version of. CA Technologies is warning that some versions of CA ARCserve Backup for Windows contain a security vulnerability (CVE-2012-1662) that could be exploited by a remote attacker to cause a denial-of-service condition to disable network services. In November 2017, a group of researchers provided a macroscopic characterization of the DoS ecosystem; they shared their findings at the AMC Internet Measurement Conference in London. Pero qué es un ataque de denegación de servicio o DDoS?. Though the Sucuri Firewall is simple to set up and protects your website immediately, it’s possible to have granular control of the WAF by using an API. But she did not choose a very favorable moment. DDOS/Botnet guide en resultaat. com as well as DNS TXT are used to store the C2 address, which is not something we see often. This is a list of all the posts we have ever made. PS : Lammer do you have a sister ? we are 12 boys here working right now so if you have it please send him like the ddos and we will care about her. Attack vectors observed include: • Volumetric DNS DDoS • Volumetric Layer 3/4 DDoS • Volumetric Layer 5-7 DDoS. Generally, a DDoS […]. Just like Anonymous and its affiliate hacker groups, they have taken to Pastebin to announce their wins. This entry is about the security of the implementation of XML-RPC by WordPress. At 22:00 on May 1st a WordPress pingback attack began targeting the Black Lives Matter website. With the help of the Pastebin statistics, we can see that this script has already been loaded more than 2 million times. org Apache Subversion (SVN). i was recently hired to update a website that was affected by this attack. On October 12, 2016, Anonymous Italia launched a cyber offensive against the Polizia Penitenziaria (the Italian penitentiary police) to protest against the "unjust" acquittal of all those involved in the trial of Stefano Cucchi's, a young Italian citizen who died in 2009 under still unclear circumstances a week after being remanded in custody by the Italian police for alleged drug dealing. You are commenting using your WordPress. Its direct and indirect aftermaths led to an unprecedented wave of cyber attacks in terms of LOIC-Based DDoS (with a brand new self service approach we will need to get used to), defacements and more hacking initiatives against several Governments and the EU Parliament, all perpetrated under the common umbrella of the opposition to SOPA, PIPA. Pastebin Hit by DDoS, Again. In the case of the September 2012 DDoS attack series, many compromised PHP Web applications were used as bots in the attacks, the company’s analysis uncovered. Hey! I'm having issues. Although DoS attacks are not a recent phenomenon, the methods and resources available to conduct and mask such attacks have dramatically evolved to include distributed (DDoS) and, more recently, distributed reflector (DRDoS) attacks—attacks […]. stateoftheinternet. Text files containing emails, passwords and other. In total there were 14000 hashes, and they looked like LANMAN hashes. Hola amigo me gusto tu articulo tiene todo lo que deberia saber, muy bueno hace tiempo que deje esto y ahora me llama la atencion de como ha crecido tanto esto, es increible, me gustaria probar aunque sea una vez la shell privada para animarme escribeme a [email protected] Contribute to BlackArch/blackarch-site development by creating an account on GitHub. The enemy of my enemy is my friend, right? Victims of the various cyber-attacks by members of the hacktivist group Anonymous are undoubtedly enjoying a bit of schadenfreude this weekend, as a new report from Symantec indicates that some Anonymous members have been tricked into downloading and running a fairly unpleasant Trojan alongside one of their distributed denial-of-service tools. Exactly this knot is used for the reflector installation. com and eBay, were targeted by DDoS attacks, and their services were stopped for hours . What is a SSDP DDoS Attack? A Simple Service Discovery Protocol (SSDP) attack is a reflection-based distributed denial-of-service (DDoS) attack that exploits Universal Plug and Play (UPnP) networking protocols in order to send an amplified amount of traffic to a targeted victim, overwhelming the target’s infrastructure and taking their web resource offline. The attackers, who call themselves the Izz ad-Din al-Qassam Cyber Fighters, launched attacks Tuesday. com放置恶意软件。. WordPress has a solid framework and follows best coding practices but hackers always seem to find their way into it by exploiting new loopholes. Continue reading. This is not something in my control. Hello! I have a very busy WordPress site (3M pageviews/month) currently running elsewhere that I'm in the process of moving over to Digital Ocean. pl"" iѕ kinda plain. Central Intelligence Agency, taking the website offline for several hours with a distributed denial-of-service attack. The attack utilized a new reflection attack vector based on the exposure of the Memcached Unix service, which was known to have security vulnerabilities, to the internet. Por Brian Krebs sobre los datos de los registradores de claves en Pastebin. UFONet is a tool designed to launch Layer 7 (HTTP/Web Abuse) DDoS attacks, using 'Open Redirect' vectors, generally located on third part-y web applications (a botnet) and other powerful DoS attacks, some including different OSI model layers, as for example the TCP/SYN flood attack, which is perform on Layer 3 (Network). In multi-stage attacks, attackers used scripts to place a backdoor that could steal information and carry out DDoS attacks. Network Edge Protection from Atlantic. 00:00:08 * krisu: quit (Ping timeout: 240 seconds): 00:00:12 niggler: where can i find this WTI stuff: 00:01:01 you can get futures numbers from CME: 00:01:17. Reflector: The Burp Plugin To Find Reflected XSS in Real Time Burp Suite extension is able to find reflected XSS on page in real-time while browsing on web-site and include some features as:. interestingly, it has. Entradas sobre ddos escritas por lekee. Prolexic Technologies, now part of Akamai, is a recognized leader in Distributed Denial of Service (DDoS) protection services, and has produced the quarterly Global DDoS Attack Report since 2011. Powerfull DDOS Attack Tool WebSites 2017 !! linux skills DDoS is short for Distributed Denial of Service. In March 2014, Akamai published a report about a widely seen exploit involving Pingback that targets vulnerable WordPress sites. Website attacks today have dramatically evolved to include both distributed (DDoS) and, more recently, distributed reflector (DRDoS) attacks that cannot be addressed by traditional on-premise solutions. by gHale | Jan 9, 2012 | Incidents. So an attacker can misuse it by creating a forged pingback request with a URL of a victim site and send it to the WordPress sites. Updated: June 6, 2016. Do not scan any devices that you do not have explicit permission to scan. Followers 0. DDoS reflection is a technique where attackers send requests with a spoofed source IP (Internet Protocol) address to third-party computers, causing them to send responses to that address instead of the original sender. com Go URL. Choisir un ˝ pastebin ˛ St´ephane Bortzmeyer Premiere r` ´edaction de cet article le 17 mars 2010. Cómo luce un ataque DDoS (vídeo) Escuchamos de ellos todo el tiempo mientras navegamos por diferentes lugares en la red. However, Anonymous believe LOIC is the reason for their people getting arrested in the last year. Last Friday, we reported on a hack that used a vulnerability in the popular WP GDPR Compliance plugin to change WordPress siteurl settings to erealitatea[. banks were attacked this week in an ongoing campaign that reflects the changing tactics used in distributed denial of service (DDoS) strikes, a security expert says. Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Pastebin Hit by DDoS, Again. |
Today, virtualization technology is ubiquitously woven into nearly every technical field and conversation, taking place in the world of Information Technology (IT), because it can provide various benefits in terms of cost effectiveness, availability, hardware utilization, resource protection, remote access, and other capability enhancements. As a result, the implications of virtual computing environments become profound and drive a shift in the fundamentals of information systems design, operation, and management. However, virtualization also introduces new challenges and concerns related to implementing secure virtualized computing environments. Therefore, in this paper we first discuss common exploits of security properties in virtualized computing environments and analyze their security vulnerabilities from the perspective of attackers. Consequently, we identify and discuss the main areas of virtualized information system design and operation in which security concerns must be addressed. Finally, we present our recommendations and future trends for trusted virtualized computing environments. |
ICANN on collision path for ccTLDs
The board of the Internet Corporation for Assigned Names and Numbers (ICANN) has signalled its intent to recommend that the same Name Collision provisions that were put in place for new generic top-level domains (gTLDs) are also applied to newly launching country-code top-level domains (ccTLDs).
By ICANN's own definition:
"a name collision occurs when users unknowingly access a name that has been delegated in the public DNS when the user’s intent was to access a resource identified by the same name in a private network. Circumstances like these, where the administrative boundaries of private and public name spaces overlap and name resolution yields unintended results, present concerns and should be avoided if possible. However, the collision occurrences themselves are not the concern, but whether such collisions cause unexpected behaviour or harm, the nature of the unexpected behaviour or harm and the severity of consequence."
The crucial part of the above definition is ‘private network’. This is where the issues of Name Collision have their roots. ‘Private networks’ is a catch-all term to describe, amongst other things, company intranets, email systems, document management systems and servers that host applications and content that is only available to users of the network.
Private networks operate on similar principles to those of the public Internet. That is to say, IP addresses are used to direct users of the network to the correct resource on the network, be that the company intranet or an application hosted within the network. Just like on the public Internet, private networks make use of "domain names" which are mapped over IP addresses when building a private network, writing software and applications and deploying these to the network.
However, the key difference between private networks and the public Internet is that, when domain names are used within the private network, they are often not actual registered domain names or Fully Qualified Domain Names (FQDNs). Often developers and those constructing a network would use fictional domain names to direct resources. This was not problematic as the fictional domain names would use extensions that did not exist in the global Domain Name System (DNS).
As domain names with these extensions could not exist in the global DNS, developers and network administrators knew that it would not be possible for their systems to leak out data to the public Internet, as such queries would be unable to resolve and thus all of the private traffic on their networks would stay within their private networks.
However, as new domain name extensions are delegated to the global DNS, the possibility for FQDNs to be registered that correspond with fictional domain names used in private networks significantly increased and thus ICANN's Name Collision provisions came into being.
As a result of this, all new gTLD registries had to either block a pre-determined list of problematic domain names from registration or wildcard their TLD for a period of 90 days in order to capture any private network traffic that was leaking into the global DNS as a result of the delegation of that gTLD.
ICANN is now proposing to make a recommendation that newly launching ccTLD registries also implement the same Name Collision measures. As a result of this, ICANN has instructed the Country-Code Names Supporting Organisation to start a study to determine the impact of Name Collisions associated with the launch of new ccTLD extensions.
David Taylor and Daniel Madden, Hogan Lovells LLP, Paris
Copyright © Law Business ResearchCompany Number: 03281866 VAT: GB 160 7529 10 |
Self-driving vehicles are becoming increasingly popular. Because they’re connected to the internet, autonomous vehicles are susceptible to being hacked. One of the easiest ways for a hacker to infiltrate an autonomous vehicle is through “GPS spoofing,” or when they use radio signals to disrupt the car’s navigation system. This method tricks the car into thinking […]
Email attachments can be dangerous. They might contain malware that causes an infection when downloaded. Even if you get an attachment from someone you know, think about it before opening it and, if you’re unsure if it’s genuine, follow up with that person separately. Cyberattackers have become skilled at spoofing return addresses to make it […]
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FireEye released a Free automated analysis tool FLASHMINGO, which enables malware analysts to detect suspicious flash samples and to investigate them.
The tool integrates various analysis workflows as a stand-alone application or as a powerful library and it can be extended via Python plug-ins.
Adobe flash remains as the most exploited software by attackers, it has more than one thousand CVEs assigned till date and almost nine hundred of these vulnerabilities have CVSS score near of nine or higher.
“We must find a compromise between the need to analyze Flash samples and the correct amount of resources to be spent on a declining product. To this end, we developed FLASHMINGO, a framework to automate the analysis of SWF files,” read FireEye blog post.
FLASHMINGO leverages the open source framework SWIFFAS to parse the Flash files. With FLASHMINGO all the binary data and bytecode are parsed and stored as SWFObject.
The SWFObject contains a list of tags that include information about all methods, strings, constants and embedded binary data, to name a few.
The tool is a collection of plug-ins that cover a wide range of common analysis that operates SWFObject and extracts the following information.
- Find suspicious method names. Many samples contain method names used during development, like “run_shell” or “find_virtualprotect”. This plug-in flags samples with methods containing suspicious substrings.
- Find suspicious constants. The presence of certain constant values in the bytecode may point to malicious or suspicious code. For example, code containing the constant value 0x5A4D may be shellcode searching for an MZ header.
- Find suspicious loops. Malicious activity often happens within loops. This includes encoding, decoding, and heap spraying. This plug-in flags method containing loops with interesting operations such as XOR or bitwise AND. It is a simple heuristic that effectively detects most encoding and decoding operations, and otherwise, the interesting code to further analyse.
- Retrieve all embedded binary data.
- A decompiler plug-in that uses the FFDEC Flash Decompiler. This decompiler engine, written in Java, can be used as a stand-alone library. Since FLASHMINGO is written in Python, using this plug-in requires Jython to interoperate between these two languages.
FLASHMINGO can be extended by adding your own plug-in, it has all the plug-ins listed under the plug-ins directory, you can copy your plugin to the template directory, rename it, and edit its manifest and code.
“Even though Flash is set to reach its end of life at the end of 2020 and most of the development community has moved away from it a long time ago, we predict that we’ll see Flash being used as an infection vector for a while.”
FLASHMINGO offers malware analysts a flexible framework to deal with Flash samples, you can download the tool from the GitHub Repository. |
This course is designed to prepare analysts to triage and derive meaningful, actionable information from alerts on FireEye File Protect.
In a hands-on lab environment, learners will be presented with various alert types and real-world scenarios in which they will conduct in-depth analysis on the behavior and attributes of malware to assess real-world threats.
After completing this course, learners should be able to:
- Recognize current malware threats and trends
- Understand the threat detection and prevention capabilities of your FireEye Security Solution
- Locate and use critical information in a FireEye alert to assess a potential threat
- Examine OS and file changes in alert details to identify malware behaviors and triage alerts
- Identify Indicators of Compromise (IOCs) in a FireEye alert and use them to identify compromised hosts
Seats for our public ILT sessions can be purchased online; refer to our public training schedule for more information.
Private training sessions are available for teams of 5 or more. Please contact your FireEye account manager for availability and pricing.
Who Should Attend
Security professionals, incident responders and FireEye analysts.
A working understanding of networking and network security, the Windows operating system, file system, registry, and use of the command line interface (CLI).
Instructor-led sessions are typically a blend of lecture and hands-on lab activities.
- FireEye Core Technology
- Malware infection lifecycle
- MVX engine
- Appliance analysis phases
- Threats and Malware Trends
- Malware overview and definition
- Motivations of malware
- Mandiant Attack Lifecycle
- Types of Malware
- Threat Management
- Features and functions of the FireEye File Protect
- Appliance Web UI
- Alert overview
- OS Changes
- File and folder actions
- Code injection
- Windows registry events
- Network access
- User Account Access (UAC)
- Malware Objects
- Malware object alerts
- BOT Communication Details
- OS Change Details for malware objects
- Malware object origin analysis
- Malware Analysis Basics
- MVX Engine Review
- Static anlysis
- Dynamic Analysis
- MVX Malware Analysis
- Custom Detection Rules (optional)
- Yara Malware Framework File Signatures
- YARA on FireEye Appliances
- YARA Hexadecimal
- Regular Expressions
- Snort Rule Processing
- Enabling Snort Rules
- Creating a Snort Rule |
Recently the content distribution networks (CDNs) have been highlighted as the new network paradigm which can improve latency for Web access. In CDNs, the content location strategy and request routing techniques are important technical issues. Both of them should be used in an integrated manner in general, but CDN performance applying both these technologies has not been evaluated in detail. In this paper, we investigate the effect of integration of these techniques. For request routing, we focus on a request routing technique applied active network technology, Active Anycast, which improves both network delay and server processing delay. For content distribution technology, we propose a new strategy, Popularity-Probability, whose aim corresponds with that of Active Anycast. Performance evaluation results show that integration of Active Anycast and Popularity-Probability can hold stable delay characteristics.
Information protection schemes on mobile phones become important challenges because mobile phones have many types of private information. In general, user authentication and anomaly detection are effective to prevent attacks by illegal users. However, the user authentication can be applied only at the beginning of use and the conventional anomaly detection is not suited for mobile phones, only but computer systems. In this paper, we propose a simple and easy-to-use anomaly detection scheme on mobile phones. The scheme records the keystrokes as the mobile phone is operated, and an anomaly detection algorithm calculates a score of similarity, to detect illegal users. We implemented a prototype system on the BREW (Binary Run-time Environment for Wireless) emulator and evaluated error rates by using results from 15 testers. From experiments results, we show the proposed scheme is able to apply the anomaly detection by checking the similarity score several times.
In an ad hoc network, we cannot assume a trusted certificate authority and a centralized repository that are used in ordinary Public-Key Infrastructure (PKI). Hence a PKI system of the web-of-trust type in which each node can issue certificates to others in a self-organizing manner has been studied. Although this system is useful for ad hoc networks, it has the problem that for authentication a node needs to find a certificate-chain to the destination node. In this paper, we formally model a web-of-trust-type PKI system, define the certificate-chain discovery problem, and propose a new distributed algorithm and its modification that solve the problem. Furthermore, we propose a measure of communication cost, and according to the measure, we compare our algorithm with an existing method by numerical computation for large-size networks and by simulation on randomly generated unit disk graphs for moderate-size networks. The simulation results show that the communication cost of the proposed method is less than 10% of the existing method.
Collision Warning Systems (CWS) can help reduce the probability and severity of car accidents by providing some sort of appropriate warning to the driver through Inter-Vehicle Communication (IVC). Especially, the CWS can help avoid collision at intersections where traffic accidents are frequent (Study Group for Promotion of ASV; Traffic Bureau, 2007). A vehicle equipped with the CWS periodically broadcasts its information, and the CWS on other vehicles use the received information to alert drivers, helping them become aware of the existence of other vehicles. To avoid collision, the CWS has concrete objectives of IVC, i.e., the CWS should receive useful information accurately and in time. Many IVC protocols including our previously proposed relay control protocol (Motegi, et al., 2006) have been developed and evaluated through traditional metrics. However, instead of using such traditional metrics directly, many requirements of the intersection CWS must be considered to judge the feasibility and practicability of IVC protocols. This paper shows performance evaluation of our previous IVC protocol developed for CWS. To study the behavior of IVC protocols, we first describe a simulation methodology including performance metrics by means of reliable and timely communications. We then use such metrics to compare our IVC protocol with the flooding protocol in large-scale simulated networks. The simulation results show that our previously proposed protocol is a good candidate for real implementation because it passes all requirements of the intersection CWS.
Program transformation by templates (Huet and Lang, 1978)is a technique to improve the efficiency of programs. In this technique, programs are transformed according to a given program transformation template. To enhance the variety of program transformation, it is important to introduce new transformation templates. Up to our knowledge, however, few works discuss about the construction of transformation templates. Chiba, et al. (2006) proposed a framework of program transformation by template based on term rewriting and automated verification of its correctness. Based on this framework, we propose a method that automatically constructs transformation templates from similar program transformations. The key idea of our method is a second-order generalization, which is an extension of Plotkin's first-order generalization (1969). We give a second-order generalization algorithm and prove the soundness of the algorithm. We then report about an implementation of the generalization procedure and an experiment on the construction of transformation templates.
Rewriting induction (Reddy, 1990) is a method to prove inductive theorems of term rewriting systems automatically. Koike and Toyama(2000) extracted an abstract principle of rewriting induction in terms of abstract reduction systems. Based on their principle, the soundness of the original rewriting induction system can be proved. It is not known, however, whether such an approach can be adapted also for more powerful rewriting induction systems. In this paper, we give a new abstract principle that extends Koike and Toyama's abstract principle. Using this principle, we show the soundness of a rewriting induction system extended with an inference rule of simplification by conjectures. Inference rules of simplification by conjectures have been used in many rewriting induction systems. Replacement of the underlying rewriting mechanism with ordered rewriting is an important refinement of rewriting induction — with this refinement, rewriting induction can handle non-orientable equations. It is shown that, based on the introduced abstract principle, a variant of our rewriting induction system based on ordered rewriting is sound, provided that its base order is ground-total. In our system based on ordered rewriting, the simplification rule extends those of the equational fragment of some major systems from the literature.
We present LCP Merge, a novel merging algorithm for merging two ordered sequences of strings. LCP Merge substitutes string comparisons with integer comparisons whenever possible to reduce the number of character-wise comparisons as well as the number of key accesses by utilizing the longest common prefixes (LCP) between the strings. As one of the applications of LCP Merge, we built a string merge sort based on recursive merge sort by replacing the merging algorithm with LCP Merge and we call it LCP Merge sort. In case of sorting strings, the computational complexity of recursive merge sort tends to be greater than O(n lg n) because string comparisons are generally not constant time and depend on the properties of the strings. However, LCP Merge sort improves recursive merge sort to the extent that its computational complexity remains O(n lg n) on average. We performed a number of experiments to compare LCP Merge sort with other string sorting algorithms to evaluate its practical performance and the experimental results showed that LCP Merge sort is efficient even in the real-world.
In this paper, we propose d-ACTM/VT, a network-based worm detection method that effectively detects hit-list worms using distributed virtual AC tree detection. To detect a kind of hit-list worms named Silent worms in a distributed manner, d-ACTM was proposed. d-ACTM detects the existence of worms by detecting tree structures composed of infection connections as edges. Some undetected infection connections, however, can divide the tree structures into small trees and degrade the detection performance. To address this problem, d-ACTM/VT aggregates the divided trees as a tree named Virtual AC tree in a distributed manner and utilizes the tree size for detection. Simulation result shows d-ACTM/VT reduces the number of infected hosts before detection by 20% compared to d-ACTM.
Previous research examined how extrinsic and intrinsic factors influence customers to shop online. Conversely, the impact of these factors on customer retention in Internet shopping has not been examined. This study is one of the few attempts to investigate the perceived benefit factors effecting customers' continuance of purchasing items through the Internet. According to an online questionnaire filled out by 1, 111 online customers to conduct a multiple regression analysis, extrinsic benefits measured in terms of time and money savings, social adjustment, and self-enhancement as well as intrinsic benefits measured in terms of pleasure and novelty as well as fashion involvement have strong effects on the continuance of purchasing. Our findings indicate that customer retention must be promoted in Internet shopping by guaranteeing not only extrinsic benefits but also intrinsic benefits. This study discusses the relevant techniques providing those benefits to customers and guidelines for future research.
Since Semantic Web is increasing in size and variety of resources, it is difficult for users to find the information that they really need. Therefore, it is necessary to provide an efficient and precise method without explicit specification for the Web resources. In this paper, we proposed the novel approach of integrating four processes for Web resource categorization. The processes can extract both the explicit relations extracted from the ontologies in a traditional way and the potential relations inferred from existing ontologies by focusing on some new challenges such as extracting important class names, using WordNet relations and detecting the methods of describing the Web resources. We evaluated the effectiveness by applying the categorization method to a Semantic Web search system, and confirmed that our proposed method achieves a notable improvement in categorizing the valuable Web resources based on incomplete ontologies.
Future networks everywhere will be connected to innumerable Internet-ready home appliances. A device accepting connections over a network must be able to verify the identity of a connecting device in order to prevent device spoofing and other malicious actions. In this paper, we propose a security mechanism for an inter-device communication. We state the importance of a distingushing and binding mechanism between a device's identity and its ownership information to realize practical inter-device authentication. In many conventional authentication systems, the relationship between the device's identity and the ownership information is not considered. Therefore, we propose a novel inter-device authentication framework guaranteeing this relationship. Our prototype implementation employs a smart card to maintain the device's identity, the ownership information and the access control rules securely. Our framework efficiently achieves secure inter-device authentication based on the device's identity, and authorization based on the ownership information related to the device. We also show how to apply our smart card system for inter-device authentication to the existing standard security protocols.
Peer-to-Peer multimedia streaming is expected to grow rapidly in the near future. Packet losses during transmission are a serious problem for streaming media as they result in degradation of the quality of service (QoS). Forward Error Correction (FEC) is a promising technique to recover the lost packets and improve the QoS of streaming media. However, FEC may degrade the QoS of all streaming due to the increased congestion caused by the FEC overhead when streaming sessions increase. Although streaming media can be categorized into live and on-demand streaming contents, conventional FEC methods apply the same FEC scheme for both contents without distinguishing them. In this paper, we clarify the effective ranges where each conventional FEC and Retransmission scheme works well. Then, we propose a novel FEC method that distinguishes two types of streaming media and is applied for on-demand streaming contents. It can overcome the adverse effect of the FEC overhead in on-demand streaming contents during media streaming and therefore reduce the packet loss due to the FEC overhead. As a result, the packet loss ratios of both live and on-demand streaming contents are improved. Moreover, it provides the QoS according to the requirements and environments of users by using layered coding of FEC. Thus, packet losses are recovered at each end host and do not affect the next-hop streaming. The numerical analyses show that our proposed method highly improves the packet loss ratio compared to the conventional method.
The performance of a network server is directly influenced by its network I/O management architecture, i.e., its network I/O multiplexing mechanism. Existing benchmark tools focus on the evaluation of high-level service performance of network servers that implement specific application-layer protocols or the evaluation of low-level communication performance of network paths. However, such tools are not suitable for performance evaluation of server architectures. In this study, we developed a benchmark tool for network I/O management architectures. We implemented five representative network I/O management mechanisms as modules: multi-process, multi-thread, select, poll, and epoll. This modularised implementation enabled quantitative and fair comparisons among them. Our experimental results on Linux 2.6 revealed that the select-based and poll-based servers had no performance advantage over the others and the multi-process and multi-thread servers achieved a high performance almost equal to that of the epoll-based server.
As increasing clock frequency approaches its physical limits, a good approach to enhance performance is to increase parallelism by integrating more cores as coprocessors to general-purpose processors in order to handle the different workloads in scientific, engineering, and signal processing applications. In this paper, we propose a many-core matrix processor model consisting of a scalar unit augmented with b×b simple cores tightly connected in a 2D torus matrix unit to accelerate matrix-based kernels. Data load/store is overlapped with computing using a decoupled data access unit that moves b×b blocks of data between memory and the two scalar and matrix processing units. The operation of the matrix unit is mainly processing fine-grained b×b matrix multiply-add (MMA) operations. We formulate the data alignment operations including matrix transposition and skewing as MMA operations in order to overlap them with data load/store. Two fundamental linear algebra algorithms are designed and analytically evaluated on the proposed matrix processor: the Level-3 BLAS kernel, GEMM, and the LU factorization with partial pivoting, the main step in solving linear systems of equations. For the GEMM kernel, the maximum speed of computing measured in FLOPs/cycle is approached for different matrix sizes, n, and block sizes, b. The speed of the LU factorization for relatively large values of n ranges from around 50-90% of the maximum speed depending on the model parameters. Overall, the analytical results show the merits of using the matrix unit for accelerating the matrix-based applications.
Skeletal parallel programming makes both parallel programs development and parallelization easier. The idea is to abstract generic and recurring patterns within parallel programs as skeletons and provide them as a library whose parallel implementations are transparent to the programmer. SkeTo is a parallel skeleton library that enables programmers to write parallel programs in C++ in a sequential style. However, SkeTo's matrix skeletons assume that a matrix is dense, so they are incapable of efficiently dealing with a sparse matrix, which has many zeros, because of duplicated computations and commutations of identical values. This problem is solved by re-formalizing the matrix data type to cope with sparse matrices and by implementing a new C++ class of SkeTo with efficient sparse matrix skeletons based on this new formalization. Experimental results show that the new skeletons for sparse matrices perform well compared to existing skeletons for dense matrices.
We study the control operators “control” and “prompt” which manage part of continuations, that is, delimited continuations. They are similar to the well-known control operators“shift” and “reset”, but differ in that the former is dynamic, while the latter is static. In this paper, we introduce a static type system for “control”and “prompt” which does not use recursive types. We design our type system based on the dynamic CPS transformation recently proposed by Biernacki, Danvy and Millikin. We also introduce let-polymorphism into our type system, and show that our type system satisfies several important properties such as strong type soundness.
We present a novel algorithm to predict transmembrane regions from a primary amino acid sequence. Previous studies have shown that the Hidden Markov Model (HMM) is one of the powerful tools known to predict transmembrane regions; however, one of the conceptual drawbacks of the standard HMM is the fact that the state duration, i.e., the duration for which the hidden dynamics remains in a particular state follows the geometric distribution. Real data, however, does not always indicate such a geometric distribution. The proposed algorithm utilizes a Generalized Hidden Markov Model (GHMM), an extension of the HMM, to cope with this problem. In the GHMM, the state duration probability can be any discrete distribution, including a geometric distribution. The proposed algorithm employs a state duration probability based on a Poisson distribution. We consider the two-dimensional vector trajectory consisting of hydropathy index and charge associated with amino acids, instead of the 20 letter symbol sequences. Also a Monte Carlo method (Forward/Backward Sampling method) is adopted for the transmembrane region prediction step. Prediction accuracies using publicly available data sets show that the proposed algorithm yields reasonably good results when compared against some existing algorithms.
This paper proposes a novel clustering method based on graph theory for analysis of biological networks. In this method, each biological network is treated as an undirected graph and edges are weighted based on similarities of nodes. Then, maximal components, which are defined based on edge connectivity, are computed and the nodes are partitioned into clusters by selecting disjoint maximal components. The proposed method was applied to clustering of protein sequences and was compared with conventional clustering methods. The obtained clusters were evaluated using P-values for GO(GeneOntology) terms. The average P-values for the proposed method were better than those for other methods.
Protein-protein interactions play an important role in a number of biological activities. We developed two methods of predictingprotein-protein interaction site residues. One method uses only sequence information and the other method uses both sequence and structural information. We used support vector machine (SVM) with a position specific scoring matrix (PSSM) as sequence information and accessible surface area(ASA) of polar and non-polar atoms as structural information. SVM is used in two stages. In the first stage, an interaction residue is predicted by taking PSSMs of sequentially neighboring residues or taking PSSMs and ASAs of spatially neighboring residues as features. The second stage acts as a filter to refine the prediction results. The recall and precision of the predictor using both sequence and structural information are 73.6% and 50.5%, respectively. We found that using PSSM instead of frequency of amino acid appearance was the main factor of improvement of our methods.
Comparative analysis of organisms with metabolic pathways gives important information about functions within organisms. In this paper, we propose a new method for comparing the metabolic pathways with reaction structures that include important enzymes. In this method, subgraphs from pathways that include `choke point' or `load point' are extracted as important “reaction structures, ” and a “reaction structure profile, ” which represents whether extracted reaction structures are observed in the metabolic pathway of other organisms, is created. Distance regarding function within organisms between species is defined using the “reaction structure profile.”By applying the proposed method to the metabolic networks of 64 representative organisms selected from Archaea, Eubacteria and Eukaryote in the KEGG database, we succeed in reconstructing a phylogenetic tree, and confirm the effectiveness of the method.
Chemical and biological activities of compounds provide valuable information for discovering new drugs. The compound fingerprint that is represented by structural information of the activities is used for candidates for investigating similarity. However, there are several problems with predicting accuracy from the requirement in the compound structural similarity. Although the amount of compound data is growing rapidly, the number of well-annotated compounds, e.g., those in the MDL Drug Data Report (MDDR)database, has not increased quickly. Since the compounds that are known to have some activities of a biological class of the target are rare in the drug discovery process, the accuracy of the prediction should be increased as the activity decreases or the false positive rate should be maintained in databases that have a large number of un-annotated compounds and a small number of annotated compounds of the biological activity. In this paper, we propose a new similarity scoring method composed of a combination of the Tanimoto coefficient and the proximity measure of random forest. The score contains two properties that are derived from unsupervised and supervised methods of partial dependence for compounds. Thus, the proposed method is expected to indicate compounds that have accurate activities. By evaluating the performance of the prediction compared with the two scores of the Tanimoto coefficient and the proximity measure, we demonstrate that the prediction result of the proposed scoring method is better than those of the two methods by using the Linear Discriminant Analysis (LDA) method. We estimate the prediction accuracy of compound datasets extracted from MDDR using the proposed method. It is also shown that the proposed method can identify active compounds in datasets including several un-annotated compounds.
The number of biological databases has been increasing rapidly as a result of progress in biotechnology. As the amount and heterogeneity of biological data increase, it becomes more difficult to manage the data in a few centralized databases. Moreover, the number of sites storing these databases is getting larger, and the geographic distribution of these databases has become wider. In addition, biological research tends to require a large amount of computational resources, i.e., a large number of computing nodes. As such, the computational demand has been increasing with the rapid progress of biological research. Thus, the development of methods that enable computing nodes to use such widely-distributed database sites effectively is desired. In this paper, we propose a method for providing data from the database sites to computing nodes. Since it is difficult to decide which program runs on a node and which data are requested as their inputs in advance, we have introduced the notion of “data-staging” in the proposed method. Data-staging dynamically searches for the input data from the database sites and transfers the input data to the node where the program runs. We have developed a prototype system with data-staging using grid middleware. The effectiveness of the prototype system is demonstrated by measurement of the execution time of similarity search of several-hundred gene sequences against 527 prokaryotic genome data.
We accelerate the time-consuming iterations for projective reconstruction, a key component of self-calibration for computing 3-D shapes from feature point tracking over a video sequence. We first summarize the algorithms of the primal and dual methods for projective reconstruction. Then, we replace the eigenvalue computation in each step by the power method. We also accelerate the power method itself. Furthermore, we introduce the SOR method for accelerating the subspace fitting involved in the iterations. Using simulated and real video images, we demonstrate that the computation sometimes becomes several thousand times faster.
This paper proposes a novel method, Hierarchical Importance Sampling (HIS) that can be used instead of population convergence in evolutionary optimization based on probability models (EOPM)such as estimation of distribution algorithms and cross entropy methods. In HIS, multiple populations are maintained simultaneously such that they have different diversities, and the probability model of one population is built through importance sampling by mixing with the other populations. This mechanism can allow populations to escape from local optima. Experimental comparisons reveal that HIS outperforms general EOPM. |
A company’s domain name equates to the company’s reputation and this is why it needs to be protected from tampering online. For this reason, companies need to use a free dns blocker to protect their domain.
There are numerous strategies to block unwanted outgoing DNS requests, such as the use of a free DNS blocker. Other examples of a DNS block is by browser settings and extensions and .hosts files. While free DNS blocker won't influence direct accesses to a numeric IP address, free DNS blocker can be easily executed to block entire domains and their subdomains, for a whole network rather than per application or per gadget.
DNS may not be the place to do such blocking, but rather free DNS blocker can be very effective, particularly as a major aspect of an in-depth defense strategy.
Although for simple privacy and security blocking purposes, you can use a free DNS blocker so it might configure the DNS server to return an error page or non-existent domain for blacklisted domains and subdomains.
DNS blocking is performed by a free DNS blocker for malicious domains using three classifications:
In many cases, the nonexistent domain response is simpler to implement by free DNS blocker. However, non-existent domain makes it hard to provide input to users who might click on malicious links or attempt to work around the block, not knowing it is a security violation. These three choices give you a variety of decisions in planning your malicious communications so that you are able not only to limit risk but also to recover devices that are likely infected.
Some of the advantages that you can expect from a free DNS blocker:
There are also some disadvantages that you can expect from a free DNS blocker:
DNS provides a phonebook-like lookup of Web resources. A free DNS blocker denies the phonebook lookup or responds in a way that disables communication for a specific web asset. In this sense, free DNS blocker gives a significant defense against multiple phases of the cyber kill chain (CKC), which depicts the stages of a cyber attack.
Free DNS blocker continues to play a critical role in the cybersecurity capabilities value chain. The higher end capabilities that do complex work like machine-learning will keep on benefitting from indicators such as DNS blacklists. Every enterprise should explore its role and its pertinent approach to use free DNS blocker and enable DNS blocking.
Techniques used by cybercriminals keep on evolving, using more application layer attacks supported by a complex set of tools. It is essential for an enterprise defense strategy to be timely, cost-effective, and dynamic to keep on protecting its system and data.
By using a free DNS blocker, cybercriminals can’t look for the critical files they need, can’t get instructions from outside, and can’t create command and control just like what they are doing before.
A free DNS blocker, like Comodo Dome, is obviously one such capability to initiate and mitigate the risks related to cyber threats. Comodo Dome delivers complete web and email protection against developing threats by giving a particular DNS block.
Comodo Dome is a web platform that is delivered as a Security-as-a-Service (SaaS) cloud infrastructure, consolidating progressive features such as unknown file containment, advanced threat protection, web security, sandboxing, antispam, DLP, Next Generation Firewall, bandwidth management, and a secure VPN service.
By looking at the source of DNS query and plan of each DNS block, Comodo Dome can recognize even the trickiest malware.
By blocking malicious DNS queries, Comodo Dome can prevent parallel movement that allows cybercriminals to maliciously use the properties of DNS. The free DNS blocker also protects against suspicious north-south traffic, preventing its most constant threats as a trigger for action.
As cutting-edge threats develop more intense ways to compromise security in the most common infrastructure systems, enterprises and individuals need to look for the best cybersecurity assets wherever they can. Comodo Dome takes cybersecurity to the next level by using the DNS infrastructure as protection against this new breed of attacks. Start your FREE trial now! |
Whois: Whois is a query and response protocol used to search for the registered users and information of an Internet resource. Every domain has required data, such as a name, IP address, and autonomous system. This information is recorded and stored within the Whois network. The system is largely used to check registration data.
The Whois system was created in the early 1980s to look up people, domains, and related resources. At the time, all domain registration was handled by the Defense Advanced Research Projects Agency (DARPA), an agency of the United States Department of Defense that largely develops new technology to be used by the military. Because there was only one domain registrar, a person could usually find domain owner data simply by entering a person's last name into the system. However, domain registry eventually expanded to commercial, third party entities, complicating the Whois process. Moreover, many people today try to hide their information by working with domain registrars who allow domain owners, or even by using fake Whois data, a tactic popular with large scale spammers. As a result, it is now necessary to know which Whois server the information being researched is located on. As a result, tools that perform Whois proxy searches have become quite common. |
Customer creates the backlog story in JIRA.
The developers commit software changes in the AWS CodeCommit.
Any commit generates AWS CloudWatch logs, which in turn generates AWS notifications. There are different topics configured for different repositories.
Respective code pipeline is activated, which in turn triggers the build followed by automated tests. The CodeBuild creates the build artifact and pushes to AWS S3.
On successful execution of the automated test, the code deployed in AWS Lambda.
Customer is notified on successful deployment. |
This area can make reference to 4 seminal programming systems that were made for learning, And that i strongly suggest studying each of them.
A typical Stay-coding ecosystem presents the learner with code over the remaining, and the output of your code on the correct. When the code is altered, the output updates instantaneously.
Shorter, informal discussion of the nature on the weak spot and its repercussions. The dialogue avoids digging way too deeply into specialized depth.
When the set of acceptable objects, for instance filenames or URLs, is proscribed or known, produce a mapping from a list of fixed enter values (for instance numeric IDs) to the particular filenames or URLs, and reject all other inputs.
Attackers can bypass the shopper-side checks by modifying values after the checks are actually performed, or by shifting the shopper to get rid of the consumer-side checks solely. Then, these modified values can be submitted to the server.
Also, a very well-developed program will not be basically a bag of functions. A superb system is built to encourage certain means of imagining, with all features carefully and cohesively intended all over that function.
Think all input is destructive. Use an "take identified fantastic" enter validation approach, i.e., use a whitelist of acceptable inputs that strictly conform to requirements. Reject any input that does not strictly conform to requirements, or rework it into a thing that does. Don't depend solely on on the lookout for malicious or malformed inputs (i.e., will not count on a blacklist). Having said that, blacklists can be practical for detecting opportunity attacks or identifying which inputs are so malformed that they need to be turned down outright.
Now, envision When your cookbook advised you that randomly hitting unlabeled buttons was the way you learn cooking.
That way, a successful assault will not likely right away provide the attacker use of the remainder of the software program or its ecosystem. For example, database programs almost never should operate as the databases administrator, especially in day-to-day operations.
Also, it can not be Employed in instances through which self-modifying code is necessary. Finally, an attack could nevertheless find out cause a denial of service, due to the fact The everyday response will be to exit the applying.
Now this facility will make it easier to discover the closest station as they are able to Find online by mobile applications also.
Together with code advancement time, other variables like view website discipline assistance charges and quality assurance also figure in into the return on financial commitment. Pair programming might theoretically offset these expenses by minimizing defects during the systems.[three]
This visualization makes it possible for the programmer to begin to see the "shape" of the algorithm, and realize it at a better level. The program flow is no more "a single line immediately after One more", over here but a pattern of traces as time passes.
Run your code in the "jail" or very similar sandbox environment that enforces demanding boundaries among the method plus the running process. This will likely properly limit which information may be accessed in a particular Listing or which commands is often executed by his response your software. OS-level illustrations involve the Unix chroot jail, AppArmor, and SELinux. On the whole, managed code could supply some protection. Such as, java.io.FilePermission during the Java SecurityManager helps you to specify constraints on file operations. |
12 Feb ZERO TRUST – WHAT DOES IT MEAN?
Zero trust security models assume there are threat actors both inside and outside a network, and no access should be implicitly trusted. That goes beyond perimeter-based security approaches that rely on firewalls to prevent breaches. Instead, zero trust verifies all resource access continually and enforces strict identity, data and device security across applications and ecosystems.
In legacy perimeter models, users or systems that gain network entry through point authentication are free to then access approved resources without undergoing further identity checks. Once the barrier is breached, internal lateral attacker movement becomes difficult to control. Zero trust architectures mitigate this by treating even legitimate users as potential threats continuously.
Security principles dictate that mere location on a network does not determine level of access. Regardless of whether inside or outside the network perimeter, users have least privilege and can only access specific resources after passing dynamic authentication hurdles per attempt. Instead of static network checkpoints, micro-segmentation and granular access policies lock down data and workflows.
Multi-factor authentication (MFA), centralized identity provider management, end-to-end encryption and analytics-driven risk scoring govern access control decisions. Users must prove identity each session via rotating credentials on company-approved and secured devices before interacting with applications holding sensitive data. Firewalls and gateways still exist in zero trust models but serve mostly to enforce identity policies instead of acting as an entry barrier.
Zero trust increases visibility into all assets, users and network behaviors via unified logging, analytics, and automation. Suspicious activity triggers alerts and containment workflows. Practices like deceptively tagging files (“honeytokens”) further help detect unauthorized handling. That allows finding threats faster amid expanding cloud ecosystems, IoT and remote workforces operating outside the conventional perimeter.
The zero trust maxim of “never trust, always verify” provides a security-first approach suitable for application environments and workforces becoming more distributed and dynamic today due to digital transformation trends. The point is to neutralize attack vectors by removing assumptions and continuously validating connections. |
Innate immunity using an unsupervised learning approach anomaly-based intrusion detection systems (ids) have been broadly researched as defensive p matzinger, “essay 1: the danger model in its historical context,” scand. Home essays images multimedia maps conquest of africa thus the primary motivation for european intrusion was economic one way to resolve this problem was to acquire colonies and export this surplus population this led to. From the above discussion, it is apparent that ids and ips function in a complementary manner to tackle problems pertaining to network 'intrusion', which is the. Thesis in intrusion detection busy market essay fc thesis in intrusion an approach for anomaly based intrusion detection system using snort slideshare.
The current approach to security is based on perimeter defense and relies on intruder has to do damage, the intrusion tolerance approach is likely to provide in summary, the ef from the traditional approach is treated at.
In this thesis i am going to model a ids using time series techniques for wireless ad hoc network by which it can detect intruders time series.
An alternate method to hids would be to provide nids type functionality at the network interface (nic) level of an end-point (either. Automated intrusion detection systems have a number of weaknesses they can be too sensitive, falsely reporting that an intrusion is under way, for example if a.
A simple statistical analysis approach for intrusion actual network traffic from the intrusion detection system is the paper is ended with a summary ii. Mercury intrusion porosimetry (mip) has been utilized for decades to obtain the pore size, pore volume and pore structure of variable porous.
Intrusion detection techniques based on machine learning and soft- computing techniques enable and network traffic summary logs intrusion detection is.
A good introduction to such methods is [hb95], from which this section intrusion techniques: pre-emption, prevention, deterrence, detection deflection, summary the dichotomy between anomaly detection and signature detection that is. Not all threats, goals, vulnerabilities, and methods are discussed because various tools are available to help detect intrusions, damage or alterations, and.
An intrusion detection system (ids) is a device, typically another an attacker will try to modify a basic attack in such a way that it will not match.Download |
Erlang B is used in a blocking system and Erlang C is used in a queueing system. With Erlang B the assumption is that an arriving call is either accepted into the system (is assigned a resource) or it is lost (blocked, sent to treatment, etc.) With Erlang C a call can queue for a period of time to see if a channel becomes free. If the time expires, the call is blocked. Erlang B is used in most public telecom networks (trunk provisioning, cell site provisioning, etc.) Erlang C is used in some networks where people call in to speak to a customer service rep (or something like that.)
I hope this helps. |
So far as the ransomware is worried, it merely prevents the consumer from accessing the telephone display. Not like different ransomwares, this one doesn’t encrypt the system. It merely freezes the display with a message that claims to be from a legislation enforcement company and ask for a high-quality to unlock the display.
This ransomware takes benefit of the “name” notification and once they get an incoming name the ransomware will get activated. Additionally, the second the consumer presses the house button or current app button, the display will get locked with the message.
“As with most Android ransomware, this new risk doesn’t truly block entry to recordsdata by encrypting them. As a substitute, it blocks entry to gadgets by displaying a display that seems over each different window, such that the consumer can’t do anything. The mentioned display is the ransom notice, which accommodates threats and directions to pay the ransom,” defined Microsoft.
The report means that the code of the malware is straightforward and it may well simply unfold to a number of telephones. Customers are beneficial to keep away from downloading apps from unknown sources. Whereas there isn’t any proof that this ransomware steals private data or not, it has been confirmed that your Android telephone might turn out to be nearly ineffective.
“This new cellular ransomware variant is a vital discovery as a result of the malware reveals behaviors that haven’t been seen earlier than and will open doorways for different malware to observe,” it added. |
The earliest forms of access control systems assigned privileges to users. These early access control systems allowed the system administrator to enable defined privileges for users like Bob and Doug.
The addition of user groups improved that situation. The system administrator could now assign privileges to groups such as Sales or Accounting and add users into those groups.
Role Based Access Control (RBAC) is the next evolutionary step in access control.
Role Based Access Control (RBAC) enables privileges to be assigned to arbitrary roles. Those roles can then be assigned to real users.
The provides more granular control of privileges, which enhances system security. In addition, it reduces the amount of administrative effort required to add or delete system users.
Role Based Access Control (RBAC) under Solaris
Sun Microsystems added support for Role Based Access Control (RBAC) in Solaris 8. The Solaris Role Based Access Control (RBAC) system is an excellent model to study in order to understand Role Based Access Control (RBAC) systems in general.
The building blocks of Solaris Role Based Access Control (RBAC) are Authorizations and Privileged Operations. Profiles are built from these two building blocks. These Profiles may then be added to Roles.
Authorizations are rights to perform specifically defined administration functions. Authorizations are defined in the auth_attr file.
The `auths` command is used to print the authorizations granted to a user.
# auths will solaris.audit.read
Privileged Operations are rights to execute specifically defined Solaris commands. Privileged Operations are defined in the exec_attr file.
Groups of Authorizations and Privileged Operations are known as Profiles. Profiles are defined in the prof_attr file.
The `profiles` command is used to print the profiles defined for a user.
# profiles will Audit Management, All Commands
user_attr and policy.conf
Roles are special system accounts. Roles are similar to regular system users, however roles may not log into the system. The preferred method of assuming a role is to use the `su` command.
The `roles` command is used to print the roles defined for a user.
# roles will admin |
Network Detection and Response is the latest trend in network-based cybersecurity. NDR follows years of product categories and three-letter algorithms to help define how an enterprise should consider defending itself from cybersecurity. Over the years, security has been defined by IPS, IDS, DLP, ATD, ADR, NAV, NTA, and more.
Fidelis has participated in magic quadrants, waves, market studies, and terminology changes since our first network cybersecurity solutions in the mid-2000’s. NDR culminates years of research and software advances to bring together the basic elements of security requirements: Detection and Response.
This paper demystifies NDR and helps you make sense of the key components of NDR technologies. Download this white paper to learn why NDR is not only beneficial, but necessary for gaining the cyber advantage, and how organizations can implement Fidelis solutions to detect, hunt and respond against the most advanced threats. You’ll see:
- Why Response is important in gaining the cyber advantage against your most advanced threats
- How Fidelis has been a leading provider of Network Detection & Response for years
- Why NDR should be a critical component to your cybersecurity arsenal |
Cyber criminals activity have been on the rise the last decade. After the incidents of CryptoLocker Ransomware, a new trojan (Casbaneiro) made its appearance.
Casbaneiro, also known as Metamorfo is a typical Latin American banking trojan that mostly used in Brazil and Mexico as shown in the picture below.
The trojan, by using advanced social engineering methods, displays fake pop-up windows. These pop-ups try to deceive the potential victims into entering sensitive information.
What Are the Trojan’s Capabilities?
The backdoor capabilities of this malware are typical of Latin American banking trojans. It can take screenshots and send them to its C&C server, simulate mouse and keyboard actions and capture keystrokes.
It can also download and install updates to itself, restrict access to various websites, and download and execute other executables.
Casbaneiro also collects several information about its victims. These information include the list of installed antivirus products, OS version, usernames and computer names.
Also, casbaneiro utilizes several cryptographic algorithms. The algorithms include command encryption, string encryption, payload encryption and remote configuration data encryption. All these encryptions are used to protect a different type of data.
The products the malware can potentially infect are Diebold Warsaw GAS Tecnologia (an application to protect access to online banking), Trusteer and several Latin American banking applications.
How Casbaneiro Affects Crypto Wallets?
Casbaneiro can also try to steal the victim’s cryptocurrency. It does so by monitoring the content of the clipboard and if the data seem to be a cryptocurrency wallet, it replaces them with the attacker’s own information.
Furthermore, researchers have found one of the attacker’s wallet addresses which was hardcoded in the binary.
To Protect Your Crypto From Cyber Criminals
Having antivirus always updated and using malware scanning programs like Malwarebytes is essential. Also double check the sending address before any crypto transaction.
And always, be careful where you click. Not everything in the internet are as they seem. |
The basic data transport protocol for caller ID is divided into four layers, namely,
The first three layers provide the actual data transport, and the application layer is used for caller ID-specific data and signaling for alerting the TE.
The physical layer provides the interface between the caller ID service and the analog line. The physical layer provides two main functions of data transmission of service-specific information and signaling mainly for alerting the TE.
The data transmission is performed using continuous-phase FSK modulation. Data is always sent as serial binary bits in simplex mode. Data transmission is continuous, and no carrier dropouts are allowed. The start of data transmission must not corrupt the first data bit. The data transmission is stopped immediately after the last bit of the data-link message. The FSK data is sent asynchronously at a signal level of −13.5 dBm in both ETSI and Telcordia recommendations as listed in Table 8.1. This power level is applicable at the central office. The FSK signal level may differ for each country, because of country-specific deviations of overall loudness rating (OLR) as well as because of send and receive gain/loss planning. To get a first-level understanding on ETSI and Telcordia basic specifications, a summary is given in Table 8.1. It is suggested to refer to the ETSI [ETSI ETS 300 659-1 ... |
Before installing Dr.Web Security Space, get familiar with . In addition, it is recommended that you do the following:
•Install all critical updates released by Microsoft for the OS version used on your computer (detailed information about ). If the operating system is no longer supported, then upgrade to a newer operating system.
•Check the file system with system utilities and remove the detected problems.
•Remove any anti-virus software from your computer to prevent possible incompatibility of Dr.Web components.
•In case of installation of Dr.Web Firewall, uninstall all other firewalls from your computer.
•Close all active applications.
There are two installation modes of Dr.Web anti-virus software:
•Command line mode |
JB via www.geek.com, 4 months, 2 weeks ago
Those of you with an Android device should be on the lookout — the security firm Dr. Web is warning users of a new trojan that disguises itself using the Google Play icon. Dubbed Android.DDoS.1.origin, the malware creates an application icon that looks just like the Google Play icon. When opened, the malware actually opens Google Play, helping disguise the malicious activity taking place in the background. Read More .. |
.or.tz General FAQ
The "TZ" code is designated for use to represent the United Republic of Tanzania, a country located in Eastern Africa. On the Internet naming system it is referred to as .TZ Country Code Top Level Domain (ccTLD). It implies therefore that all domain names ending with .TZ explicitly and uniquely identify a domain owner residing in Tanzania or having a business or service branch in Tanzania.
In 2006, Tanzania Network Information Centre (tzNIC), a non-profit company was established. .tzNIC strives to promote the utilization of .TZ domain names; enhances cpr144449003101 its technical capacity in administering and managing the .TZ registry; protect registrant's interests and harmonize the .tzccTLD management policies at National and International levels. |
Trojan:Android/Moghava repeatedly searches for and modifies JPEG images stored on the device.
Trojan:Android/Moghava was found being distributed in unofficial third party Android application websites in late 2011. Unlike most Android malware, it is not designed for monetary profit but for political ridicule.
Moghava.A.s malicious activity is triggered each time the device boots, activating a service named 'stamper'. This service waits for five minutes before searching for JPEG image files stored in the memory card, looking in the /sdcard/DCIM/Camera/ location in particular because that is where pictures taken from the device.s camera are stored.
For every found image file, it will superimpose another image on top of the original one. This routine will be repeated over and over in every five minutes, which effectively increases the size of the image file , and consumes the free space in the memory card.
This activity continues for a certain time interval before exiting.
This malware is discussed in further detail in: Q1 2012 Mobile Threat Report (PDF).
Date Created: -
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This ransomware uses a free photo upload service as its C&amp;amp;C server. This way, it is able to mask its C&amp;amp;C routines.Read more
This ransomware uses Pokemon Go probably to hide its true nature. It tries to spread copies of itself on removable drives as PokemonGo.Read more
This ransomware, also known as R980 ransomware, resembles some aspects of RANSOM_MADLOCKER as it drops files other than ransom notes. It also avoids certain file paths.Read more
This ransomware is written in Jscript, a scripting language designed for Windows. This variant comes from an .Read more
This ransomware is believed to be patterned after WALTRIX/CRYPTXXX. It almost has the same routines as the aforementioned ransomware family, save for a few minor differences.Read more
This ransomware, seemingly similar to JIGSAW ransomware, threatens to delete one file six hours after non-payment. It threatens to delete all encrypted files after 96 hours of non-payment.Read more
This ransomware is delivered as an attached document, via spam email. It disguises itself as a fake Thai customs form.Read more
This ransomware has the ability to encrypt files found on an affected system. This routine makes these files inaccessible until a ransom is paid.Read more
This ransomware is written in Jscript, a scripting language designed for Windows. Particularly, it is for Internet Explorer.Read more
This JIGSAW ransomware uses chat support to aid customers in paying the demanded ransom. Previous variants of JIGSAW are known to use scary or porn-related ransom messages.Read more
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Fighting modern adversaries requires having a modern security operations center (SOC), especially as organizations move to the cloud. To protect their estates against tomorrow’s threats, security professionals have often turned to more data sources and adding more security monitoring tools in their operations, both in the pursuit of maximizing their attack surface visibility and reducing time to detect and respond to threats.more →
The road to next-gen SOC with SOAR security
А cyber attack is expected to happen every 11 seconds in 2021, according to Cybersecurity Ventures. This fact only underlines what cybersecurity experts have been predicting for a long time – The age of SOAR security in SOCs is already at our doorstep.more →
There are many things that can reduce the effectiveness of your SOC operations. We are going to look at what we think are the top 7 challenges that have the most impact on the efficient running of your SOC operations.
1. Volume and validity
The flood of daily alerts, many of which are false, can mean that analyst spend too much of their time hunting down information on alerts instead of identifying risk, responding to incidents, identifying incident impact, and reducing breach detection time.more →
Security teams agree their cloud infrastructures generate more security alerts than similar on-prem environments. Legacy security tools and SIEMs weren’t built for this cloud transformation and have resulted in more threat visibility gaps than ever before. So what can your organization do to defend against this continuously evolving threat landscape?more →
This month we are sharing a blog from our partner Swimlane discussing how SOAR can improve your cybersecurity.
Security orchestration, automation and response (SOAR) goes beyond automating tasks that used to be handled manually by working together to effectively, and even proactively, improve your cybersecurity operations.more →
This month we are sharing a blog from our partner eSentire that takes a look at how artificial intelligence and machine learning can help you deal with data security.
Tap AI and ML to scan security and threat logs as part of a two-pronged approach to security and threat detection
We’re now in a machine-scale world, where the scale, complexity and dynamism of data exceeds human capacity to keep up with it.more →
There is a lot of discussion going into the SIEM vs SOAR debate at the moment and it is extremely important to understand the difference between these two cyber security tools. SIEM and SOAR have several common features, and do complement each other, but we cannot use these terms interchangeably.more →
When we hear the term ‘Endpoint Security’ we often think of making sure your organization is protected from malicious actors and cyberattacks attacking via an endpoint. This involves making sure that all the access points into an organization’s critical systems and physical devices are protected from unauthorized access to prevent damage to the rest of the network.more → |
Google introduced eight new top-level domains at the beginning of May, such as .dad, .phd, .prof, .esq, .foo, .zip, .mov, and .nexus.
Over time, the nonprofit Internet Corporation for Assigned Names and Numbers (ICANN) has lifted limitations on TLDs, allowing businesses like Google to bid to sell access to more of them.
ICANN is the organization that is responsible for these TLD registrations. Domains ending with any characters like .xyz, .top, etc., are being registered by this ICANN.
The two TLDs “.mov” and “.zip” are particularly well-suited for taking phishing and other types of online fraud.
Cybercriminals have already begun using.zip names to trick people into believing they are downloadable files rather than URLs.
Avast analysis reveals that one-third of the top 30.zip domains blocked by their threat detection engines misuse the names of well-known IT firms like Microsoft, Google, Amazon, and Paypal to deceive users into thinking they are files from reputable businesses.
A few TLDs that Avast comes across practically raise some suspicion. These include, among others,.xyz,.online,.biz,.info,.ru,.life, and.site.
.Zip Domain Security Risks
Mimicking Legitimate Companies
According to Avast, a big issue here is the possibility of file confusion and the resulting difficulties in distinguishing between local and remote sources, which might represent a security risk.
For educational reasons, if a prototype email is created that makes use of the fact that the attachment and the link might refer to entirely separate destinations.
Experts say utilizing a.zip domain to trick visitors is rather simple. Furthermore, the link preview can be altered to conceal the protocol, such as HTTP(S).
The most appealing domains are those that are strongly associated with well-known, significant service providers.
These include microsoft-office[.]zip, microsoft[.]zip, csgo[.]zip, google-drive[.]zip, microsoftonedrive[.]zip, googlechrome[.]zip, and amazons3[.]zip.
Other perfect examples with a pdf keyword combined with a subdomain. Namely 226×227.pdf[.]zip, 2023-05.pdf[.].zip, cv3.pdf[.]zip, temp1_rsbu_12m2021.pdf[.]zip.
The zip domains are attractive and perhaps enticing for fraudsters to utilize, but they create an audit trail and are simple to block.
Using old WordPress installations or insecure web servers is undoubtedly more difficult than registering a domain. This is also the cause of the lesser number of prevented attacks than anticipated.
Given the enormous amount of.com domains registered, it seems reasonable that their web shield blocks the majority of.com domains. A few domains jump out when they look at the remaining data, though.
File Archiver In The Browser
A new phishing kit, “file archiver in the browser,” exploits ZIP domains by presenting fraudulent WinRAR or Windows File Explorer windows in the browser, tricking users into executing malicious files.
Security researcher mr.d0x revealed a phishing attack that involved mimicking a browser-based file archiver software like WinRAR using a .zip domain to enhance its credibility.
The toolkit enables embedding a counterfeit WinRar window in the browser, creating the illusion of opening a ZIP archive and displaying its contents when accessing a .zip domain.
This phishing toolkit may be used by threat actors to steal credentials and spread malware.
Using “chatgpt5 [.]zip” to Trick Users
Hackers also Use “chatgpt5 [.]zip” to Trick Users into Downloading Malware. Threat actors employ creative names to disguise phishing attacks, with a new TLD ‘ .ZIP’ introducing a potential threat by chatgpt5 leading to malicious sites.
With internet evolution, countless gTLDs emerged for personalized web addresses, offering branding chances but also phishing opportunities that demand alertness.
The inclusion of ‘.ZIP’ as a gTLD adds complexity to phishing detection, particularly due to its association with compressed files, increasing confusion and providing phishers with a potent new tool for their attacks.
The hype around ChatGPT lead to the creation and registration of “chatgpt5 [.]zip ” on May 20th, supposedly for the next GPT iteration, but surprisingly, it holds a neutral text message instead of malware.
To trick the users by claiming to safeguard students from malware, “assignment[.]zip” was registered by the threat actors, redirecting visitors to a download of a ZIP archive containing completely safe files.
Exploiting the widespread use of the. ZIP extension, malicious actors create campaigns and websites reminiscent of early domain squatting techniques.
Phishing Attempts Using Popular Office Software Suite Filenames
The cybersecurity company, Arctic Wolf has also detected some.zip domains that are being utilized for successful phishing attempts using popular office software suite filenames.
Based on previous phishing campaign tactics, methods, and procedures (TTPs), they anticipate that further threat actors will continue to employ these TLDs for their phishing domains in the foreseeable future.
Risk of Sensitive Information Exposure
According to Talos, domains using the “.zip” and related TLDs enhance the risk of sensitive information exposure due to accidental DNS requests or web requests.
As soon as the new “.zip” TLDs became available, internet browsers or messaging applications like Telegram started recognizing strings that ended in “.zip” as URLs and automatically hyperlinking them.
A DNS or web request may occasionally be made in chat applications to display a thumbnail of the connected website, which is particularly troublesome.
Additionally, abuse of these domains is not theoretical, with cyber intel firm Silent Push Labs already discovering what appears to be a phishing page at microsoft-office[.]zip attempting to steal Microsoft Account credentials.
These developments have sparked a debate among developers, security researchers, and IT admins, with some feeling the fears are not warranted and others feeling that the ZIP and MOV TLDs add unnecessary risk to an already risky online environment.
- Any.zip Top-Level Domains (TLDs) should be used with caution.
- Keep a tight check on the online traffic for your business, especially on the lookout for any odd activity connected to it.TLDs in zip.
- Consider putting in place extra filters for emails that include to further safeguard against possible dangers.TLDs in their content using zip.
- To guarantee that it is as effective as possible against the most recent threats, always keep your antivirus software updated.
- To keep ahead of potential risks, read security alerts and updates about developing threats frequently. |
|Response status codes|
|Security access control methods|
In the most common situation, this means that when a user clicks a hyperlink in a web browser, causing the browser to send a request to the server holding the destination web page, the request may include the Referer field, which indicates the last page the user was on (the one where they clicked the link).
The misspelling of referrer was introduced in the original proposal by computer scientist Phillip Hallam-Baker to incorporate the "Referer" header field into the HTTP specification. The misspelling was set in stone by the time (May 1996) of its incorporation into the Request for Comments standards document RFC 1945 (which 'reflects common usage of the protocol referred to as "HTTP/1.0"' at that time); document co-author Roy Fielding remarked in March 1995 that "neither one (referer or referrer) is understood by" the standard Unix spell checker of the period. "Referer" has since become a widely used spelling in the industry when discussing HTTP referrers; usage of the misspelling is not universal, though, as the correct spelling "referrer" is used in some web specifications such as the
Referrer-Policy HTTP header or the Document Object Model.
When visiting a web page, the referrer or referring page is the URL of the previous web page from which a link was followed.
More generally, a referrer is the URL of a previous item which led to this request. For example, the referrer for an image is generally the HTML page on which it is to be displayed. The referrer field is an optional part of the HTTP request sent by the web browser to the web server.
Many websites log referrers as part of their attempt to
Many blogs publish referrer information in order to link back to people who are linking to them, and hence broaden the conversation. This has led, in turn, to the rise of referrer spam: the sending of fake referrer information in order to popularize the spammer's website.
Most web servers maintain logs of all traffic, and record the HTTP referrer sent by the web browser for each request. This raises a number of privacy concerns, and as a result, a number of systems to prevent web servers being sent the real referring URL have been developed. These systems work either by blanking the referrer field or by replacing it with inaccurate data. Generally, Internet-security suites blank the referrer data, while web-based servers replace it with a false URL, usually their own. This raises the problem of referrer spam. The technical details of both methods are fairly consistent – software applications act as a proxy server and manipulate the HTTP request, while web-based methods load websites within frames, causing the web browser to send a referrer URL of their website address. Some web browsers give their users the option to turn off referrer fields in the request header.
Most web browsers do not send the referrer field when they are instructed to redirect using the "Refresh" field. This does not include some versions of Opera and many mobile web browsers. However, this method of redirection is discouraged by the World Wide Web Consortium (W3C).
If a website is accessed from a
Another referrer hiding method is to convert the original link URL to a Data URI scheme-based URL containing small HTML page with a meta refresh to the original URL. When the user is redirected from the
data: page, the original referrer is hidden.
Content Security Policy standard version 1.1 introduced a new referrer directive that allows more control over the browser's behaviour in regards to the referrer header. Specifically it allows the webmaster to instruct the browser not to block referrer at all, reveal it only when moving with the same origin etc.
- "Does your website have a leak?". ICO Blog. 2015-09-16. Archived from the original on 2018-05-24. Retrieved 2018-08-16.
- "Referrer Policy: Default to strict-origin-when-cross-origin - Chrome Platform Status". www.chromestatus.com. Retrieved 2021-03-23.
- Lee, Dimi; Kerschbaumer, Christoph. "Firefox 87 trims HTTP Referrers by default to protect user privacy". Mozilla Security Blog. Retrieved 2021-03-23.
- Wilander, John (2019-12-10). "Preventing Tracking Prevention Tracking". WebKit blog.
- Hallam-Baker, Phillip (2000-09-21). "Re: Is Al Gore The Father of the Internet?". Newsgroup: alt.folklore.computers. Retrieved 2013-03-20.
- Fielding, Roy (1995-03-09). "Re: referer: (sic)". ietf-http-wg-old (Mailing list). Retrieved 2013-03-20.
- "Network.http.sendRefererHeader". MozillaZine. 2007-06-10. Retrieved 2015-05-27.
- "HTML DOM Document referrer Property". w3schools.com. Retrieved 2013-03-20.
- "4.12 Links — HTML Living Standard: 126.96.36.199 Link type "noreferrer"". WHATWG. 2016-02-19. Retrieved 2016-02-19.
- "Content Security Policy Level 2". W3. 2014. Retrieved 2014-12-08. |
The HTTP Forwarded request header contains the IP address for the client that initiates the HTTP request.
The Forwarded request header is to inform the server concerning the originating client’s IP address, as well as the addresses of intermediaries that the HTTP request has passed through. Examples of intermediaries might be forward or reverse proxy servers, a load balancer, or a content delivery network (CDN). This HTTP header can be generated, modified, or deleted by any intermediary en-route to the server.
The information provided by the Forwarded request header can be used to facilitate troubleshooting or statistical reporting. It does, however, contribute to the erosion of privacy by exposing the originating IP address. The directives are
by directive is optional and stores information about the interface where the HTTP request entered a proxy server. It can contain a range of values including:
- A masked identifier such as hidden. This is the default value.
- An IPv4 or IPv6 address, optionally with a port
- The unknown identifier, indicating that the previous intermediary is not known but does exist.
for directive is similar to
by, with the same possible values, although it refers specifically to the client that originated the HTTP request.
host directive is the HTTP Host request header field, as it is read by the intermediary.
proto directive indicates the protocol that was used to make the HTTP request. This is normally either HTTP or HTTPS.
The HTTP Forwarded header is used to provide information to the server about the originating client’s IP address, as well as those of the intermediaries that the HTTP request passed through. |
From windows 7
We recommend using Internet Explorer.
In this case, remember:
- Put the address as a trusted site.
To do this, access Tools → Internet Options → Security Tab → Trusted Sites → Sites Button.
Once you have clicked on the Sites button, a window like the following one will appear. Uncheck the box 'Require server verification (https :) for all sites in this zone' and click the Add button, once the application URL is typed, to be added in the text field called Websites and then have it on our trusted sites.
- Enable popup windows.
Install Adobe Reader. Most reports are downloaded as pdf's.
- URL to download: https://acrobat.adobe.com/us/en/acrobat/pdf-reader.html
In the case of windows 10, this comes with edge browser, but you have to go to the features and enable the ie11. |
Data visibility often lost as virtualized workloads in the cloud grow
Cloud adoption is growing for virtualized workloads, with a majority of organizations running or planning to run virtual machines in the cloud this year.
That is one of the findings of a new report from Druva, a provider of cloud data protection and management products. According to the report, many of these enterprises have no visibility into how data management policies are being applied, however.
The company surveyed 170 IT and virtualization professionals in July 2018, and 90 percent said their organization was either running or planning to run VMs in the cloud this year. The trend is on the upswing, with 41 percent of organizations now running VMs in the cloud compared with 31 percent in 2017.
Of those organizations running or planning to run VMs in the cloud in 2018, 59 percent expect to use AWS for these workloads.
More than half of the survey respondents (54 percent) said they have no visibility into how and if data management policies are being applied and enforced, and 55 percent do not have a plan to centralize protection of their data across multi-cloud or hybrid cloud environments.
The result is a critical gap in visibility into data in the cloud, the report said, which can increase risk to data infractions and compliance issues, such as not purging data in time. |
Im wondering if you can use it as an ips, as you can in unix, where snort will drop packets that it flags. Getting snort installed successfully can be a challenge, but it is also only the first step in setting the tool up so you can launch it to start monitoring traffic and generating alerts. Installing snort on windows can be very straightforward when everything goes as planned, but with the wide range of operating system environments even within similar versions of windows, the experience of individual users can vary for a variety of technical and nontechnical reasons. Help with possible remote ports listening in windows 7. This download is licensed as freeware for the windows 32bit and 64bit operating system on a laptop or desktop pc from wifi software without restrictions.
First, you need to download and install few things. From lord of the rings, to mixmaster, to apache, to pgp, to snort, to openssl, to stackguard formatguard. Snort is an open source network intrusion prevention and detection system. It allows you to share files with friends and other people, for example, in the following scenarios with people who have a common interest from all over the world. For snort to be able to act as sniffer and ids it needs windows packet capture library which is winpcap. Installing a 3264 bit windows intrusion detection system. Snort vim is the configuration for the popular text based editor vim, to make snort configuration files and rules appear properly in the console with syntax highlighting. All tools are command line which allows for heavy scripting. Airsnort operates by passively monitoring transmissions, computing the encryption key when enough pac. As we have discussed earlier, snort rules can be defined on any operating system.
I am showing windows installation of snort on 64 bit machine1. There are many sources of guidance on installing and configuring snort, but few address installing and configuring the program on windows except for the winsnort project linked from the documents page on the snort website. Free download page for project airsnort s airsnort 0. Snort is an open source network intrusion prevention and detection system utilizing a ruledriven language, which combines the benefits of signature, protocol, and anomaly based inspection methods. Discussion in other firewalls started by ace55, may 21, 2010. The installation applet will automatically detect the operating system and install the correct drivers. After you have downloaded snort, download snort rules. Snortvim is the configuration for the popular text based editor vim, to make snort configuration files and rules appear properly in the console with syntax highlighting. How to setup snort ids system on windows 7 workstation. Snort is an opensource, free and lightweight network intrusion detection system nids software for linux and windows to detect emerging threats. Airsnort is a popular wifi hacking software used for decrypting wifi password on wifi 802.
May 28, 2012 heres a tutorial on installing snort on a windows 7 computer. This has been merged into vim, and can be accessed via vim filetypehog. Npcap is the nmap projects packet sniffing and sending library for windows. Before configuring snort, you will need to create a directory structure for snort. As you know, airsnort is a passive scanner through network. It ran as command prompt with recurring messages containing some captured packet appearing. Npcap works on windows 7 and later by making use of the new ndis 6 lightweight filter lwf api. May 17, 2019 windows users perform the following steps windows xp, belkin pcmcia and dlink pci cards in this example. Install snort on windows tcat shelbyville technical blog. Apr 29, 20 snort is an open source intrusion detection systemids for unix and windows. Snort is a network intrusion prevention system and intrustion detection system that can detect anomalies and other traffic on your network. Installing a 3264 bit windows intrusion detection system winids sign in to follow this. Snort should be a dedicated computer in your network. Airsnort for windows 7 64bit, what it is and steps to use it.
Configuring the nf file nf file is the main file in snort operation and must be configured before running snort. The only disadvantage is that this tool works for wep network and not for wap network. The application works by implementing the standard fms attack along with some optimizations such as korek attacks, as well as the ptw attack. Download32 is source for snort for windows shareware, freeware download winaxe plus ssh xserver for windows, fprot antivirus for windows, system information for windows, partition recovery for windows, data recovery software for windows, etc. The snort manual we use acid and base to view our snort system link. This is done passively by the software where it gathers packets going in and out of the system. Apr 02, 2016 download airsnort wifi hacking software. To get snort ready to run, you need to change the default configuration settings file which is created as part of the snort installation. Up to 16 million ivs, in total nine thousand of 128bit keys are weak.
Airsnort windows wireless wep crack powered by sroney. Airsnort is a wireless lan wlan tool which cracks encryption keys on 802. Heres a tutorial on installing snort on a windows 7 computer. Oct 27, 2010 how to setup snort ids system on windows 7 workstation. I ll break out the key parts of the file that you modify. Snort is an open source intrusion detection systemids for unix and windows.
To remove winpcap from the system, go to the control panel, click on addremove programs and then select winpcap. Airsnort is a wireless lan wlan tool which recovers encryption keys. This file will download from the developers website. Airsnort operates by passively monitoring transmissions, computing the. Airsnort operates by passively monitoring transmissions, computing the encryption key when enough packets have been gathered.
Apache openoffice free alternative for office productivity tools. The winpcapbased applications are now ready to work. Sniffer mode, packet logger mode, and network ids mode. Download your driver from airopeek unfortunately no longer available for download from that is matched to your wireless card manufacturer and model. When we have winpcap installed the next step will be to download snort.
It is based on the discontinued winpcap library, but with improved speed, portability, security, and efficiency. Snort is an open source network intrusion prevention and detection system utilizing a ruledriven language, which combines the benefits of signature, protocol, and. Here, we will configure snort for network ids mode. Installing snort on windows can be very straightforward when everything goes as planned, but with the wide. Inline snort on windows, with gui wilders security forums. This is the software that sits behind your firewall and looks for traffic or activity that may indicate that the firewall has failed to keep out intruders, a second line of defence. A wireless lan encryption tool used to crack wep networks on windows. Disclaimer snort is a product developed by sourcefire, inc this site is not directly affiliated with sourcefire, inc. You are able to join hubs with other users, and chat, perform searches and browse the share of each user. A lot of guis have taken advantage of this feature. Games downloads air attack by jgsportal version and many more programs are available for instant and free download. Free download page for project airsnorts airsnort0. It implements the standard fms attack along with some optimizations like korek attacks, as well as the allnew ptw attack, thus making the attack much faster compared to other wep cracking tools. Latest 3264bit windows intrusion detection systems core.
Defending your network with snort for windows tcat. Airsnort is a wireless lan tool that operates by passively monitoring transmissions and cracks encryption keys on 802. Aircrack ng is a complete suite of tools to assess wifi network security. It works primarily linux but also windows, os x, freebsd, openbsd, netbsd, as well as solaris and even ecomstation 2. In addition to all of our internal projects, shmoocon, airsnort, rainbow tables to name a few, our work extends into some of the most widely used infosec software and books.
How to install snort intrusion detection system on windows. Windows users perform the following steps windows xp, belkin pcmcia and dlink pci cards in this example. Visit snort site and download snort latest version. It comes for both windows and linux operating system.243 562 1579 1091 1540 130 1557 154 1524 49 1557 1102 466 1206 235 545 1018 607 879 832 271 713 1518 362 64 807 1394 80 1331 777 842 1119 244 1432 1025 1402 |
Part of running a business is to ensure that you protect what generates money. Often this means your files, business contracts and agreements as well as other confidential and important documents. Most of these important documents are often stored in a computer or laptop system online or offline. This can be dangerous as there are many viruses that can ultimately remove and delete files from your computer without you knowing.
A free antivirus software tool from a Finnish security company can help to detect all forms of the five different malware families used to steal online banking information. The Debank tool was originally created by Fitsec for scanning enterprise machines. It works by scanning the process memory of a computer to detect malware that is compressed before distribution.
Compressing or packing is one technique that can sometimes fool standard antivirus programs which can mistake the malware for a different program each time it is repackaged. Other antivirus programs use heuristics in addition to traditional signatures to detect malware, but this approach does not always yield as much success as performing a full sweep of memory. The Debank program analyzes the core program code which is rarely changed by malware authors. |
In order to make web applications more secure, we must secure the HTTP headers that are communicated back and forth for exchanging additional information between the communicating devices which are mostly client and server.
Let's us see the different header options which their definition and possible value that can make them secure while in transmission through the channel.
Disallow framing of other domains (X-Frame-Options) - This HTTP response header is used to control whether or not a browser should be able to render a page inside an <iframe> element. If a website is not determined to share its content in an embedded form in other sites, then it should be set with the "DENY" value. Preventing sharing your site in this way also also secure your users from clickjacking attacks.
In Web.config file, you can set this HTTP header as follows:
Prevent reflected cross site scripting (X-Xss-Protection) - This HTTP response header stops the page from loading on detecting reflected cross-site scripting attacks. In other words, we can say that it enables cross site scripting filtering.
This header can be added in Web.config as follows:
Here value="1; mode=block means instead of sanitizing the page experiencing the cross-site scripting attack, it will prevent rendering of the page.
Disable guessing MIME type by inspecting the content (X-Content-Type-Options) - Enabling this HTTP response header to "nosniff" value, restrict the browser from guessing the file type by inspecting the content rather the file will be treated of the type defined in Content-Type headers. This feature also makes it tough for hackers to get idea of the content mime type by inspecting the content.
Disable Flash from making cross-origin requests (X-Permitted-Cross-Domain-Policies) - If we are not willing to allow Flash content producers to embed your content in their products and you want to disable Flash components from making cross-origin requests, then set this header to "none" in the Web.config file as shown below.
Keep communication channel secured using HTTPS (Strict-Transport-Security) - This HTTP response header when used, enable the website to be accessed using HTTPS and not HTTP. When using HTTPS (Hypertext Transfer Protocol Secure) for data transmission, if the hacker gets access to the data even in such case h/she wouldn't be able to understand it due to the encryption applied to the information. Only sender and recipients who knows the code to decipher the message can read the information easily.
Here max-age defines time in seconds which tells browser to use this setting for 1 year (equals to 31536000 seconds) and includeSubDomains which is an optional parameter, when specified apply this rule to site's subdomain as well.
Don't share referrer details with other sites (Referrer-Policy) - If it is not required to share the referrer information to the linked websites which might be accessed through the links within your site, then its better to remove the referrer details entirely. It further prevents from exposing sensitive details in the URLs.
The same can be applied using Web.config in the following way. |
ICANN Resolutions » Recommendations for Managing the IDN variant TLDs
Important note: The Board Resolutions are as reported in the Board Meeting Transcripts, Minutes & Resolutions portion of ICANN's website. Only the words contained in the Resolutions themselves represent the official acts of the Board. The explanatory text provided through this database (including the summary, implementation actions, identification of related resolutions, and additional information) is an interpretation or an explanation that has no official authority and does not represent the purpose behind the Board actions, nor does any explanations or interpretations modify or override the Resolutions themselves. Resolutions can only be modified through further act of the ICANN Board.
Whereas, Internationalized Domain Names (IDNs) enable Internet users to access domain names in their own languages and remain a key component of ICANN's work.
Whereas, the Board recognizes that IDN variants are an important component for some IDN top-level domain (TLD) strings and that implementation of variant labels in the root zone must take place in a way that maintains the security and stability of the DNS.
Whereas, the Board resolved in 2010 that IDN variant TLDs will not be delegated until relevant work is completed and directed ICANN org to develop a report identifying what needs to be done with the evaluation, possible delegation, allocation and operation of generic top-level domains (gTLDs) containing variant characters IDNs, in order to facilitate the development of workable approaches to the deployment of gTLDs containing variant characters IDNs.
Whereas, based on six case studies, integrated into A Study of Issues Related to the Management of IDN Variant TLDs in 2012, ICANN org and the community identified two gaps to address: first that there is no definition of IDN variant TLDs, and second that there is no IDN variant TLD management mechanism.
Whereas, the Procedure to Develop and Maintain the Label Generation Rules for the Root Zone in Respect of IDNA Labels (RZ-LGR Procedure) has been developed by the community to define the IDN variant TLDs and, following the Board resolution in 2013 which approved the RZ-LGR Procedure, has been implemented to incrementally develop the Root Zone Label Generation Rules to address the first gap.
Whereas, ICANN org has developed the Recommendations for Managing IDN Variant TLDs (the Variant TLD Recommendations), a collection of six documents finalized after incorporating the public comment feedback and published them as mechanisms for addressing the second gap identified by the community in the implementation of IDN variant TLDs.
Resolved (2019.03.14.08), the Board approves the Variant TLD Recommendations and requests that the ccNSO and GNSO take into account the Variant TLD Recommendations while developing their respective policies to define and manage the IDN variant TLDs for the current TLDs as well as for future TLD applications.
Resolved (2019.03.14.09), the Board requests that the ccNSO and GNSO keep each other informed of the progress in developing the relevant details of their policies and procedures to ensure a consistent solution, based on the Variant TLD Recommendations, is developed for IDN variant ccTLDs and IDN variant gTLDs.
Resolved (2019.03.14.10), the Board also recognizes the significant community effort and contribution, since the start of the IDN Variant Issues Project in 2011, which has led to the development of the Variant TLD Recommendations.
Internationalized Domain Names (IDNs) enable people around the world to use domain names in local languages and scripts. Some script communities have identified that technically distinct domain labels may be considered indistinguishable with other domain labels and therefore the "same" labels, referred to as variant labels. The IDNs in Applications (IDNA) 2008 standard, while stipulating how to use domain names in multiple scripts, also asks in RFC 58941 that "registries should develop and apply additional restrictions as needed to reduce confusion and other problems … For many scripts, the use of variant techniques … may be helpful in reducing problems that might be perceived by users. … In general, users will benefit if registries only permit characters from scripts that are well-understood by the registry or its advisers."
Based on the IDNA2008 standard, variant labels must minimally be identified and managed to ensure that end-users are prevented from any security threats. A few of the variant labels identified could even be activated to promote usability of the IDNs, as different language communities using a script may use a different variant label. In some cases, applications for IDN ccTLDs and new gTLDs have identified additional labels considered as variant labels, indicating that the community may consider these different labels as variants of each other. However, due to lack of a clear definition and a solution to implement them, ICANN Board resolved on 25 September 2010 that "no variants of gTLDs will be delegated through the New gTLD Program until appropriate variant management solutions are developed." The resolution further directed ICANN staff to develop "an issues report identifying what needs to be done with the evaluation, possible delegation, allocation and operation of gTLDs containing variant characters IDNs as part of the new gTLD process in order to facilitate the development of workable approaches to the deployment of gTLDs containing variant characters IDNs."
Achieving these security and usability goals in a stable manner is the key challenge to be addressed. To address these complex linguistic and technical issues, ICANN organization undertook the IDN Variant Issues Project under the guidance of the ICANN Board. As a first step, it engaged with experts from six script communities, who analyzed issues in identifying variant labels for each of these scripts. This analysis of issues for Arabic, Chinese, Cyrillic, Devanagari, Greek, and Latin scripts in 2011, integrated in the Integrated Issues Report (IIR) (2012) identified two challenges:
"in the DNS environment today, there is no accepted definition for what may constitute a variant relationship between top-level labels
"nor is there a 'variant management' mechanism for the top level, although such has often been proposed as a way to facilitate solutions to a particular problem."
1. Defining Variant TLDs
IIR outlined the follow-on work that might be undertaken. To address the first problem noted in IIR, the community developed Procedure to Develop and Maintain the Label Generation Rules for the Root Zone in Respect of IDNA Labels (RZ-LGR Procedure). Based on the direction of the ICANN Board on 11 April 2013, ICANN undertook RZ-LGR Procedure which follows a two-step process, requiring each community to develop individual script-based Label Generation Rules (LGR) proposal and an expert panel to review and integrate each proposal into the Root Zone LGR (RZ-LGR). Multiple script communities have finalized their proposals, from which Arabic, Ethiopic, Georgian, Khmer, Lao and Thai script proposals have been integrated into its second version, RZ-LGR-2. Many other script communities are also active in defining their rules. Further, a specification to encode these linguistic details into a formal machine-readable format has also been developed and released through IETF as standards track RFC 7940: Representing Label Generation Rulesets Using XML. A LGR tool has also been developed to create, use and manage the LGRs, and is available for the community online as well as for download with an open source license.
2. Analyzing Variant TLD Management Mechanisms
The label generation rules for the root zone derived from the process described above produce variant TLD labels that are candidates for allocation. To address the second part of the need stated in Integrated Issues Report for a variant management mechanism for the top level, it is necessary for the ICANN community to develop the policies and procedures that govern such allocation of variant names. The set of documents, finalized after public comment and published, offer recommendations for consideration by the ccNSO and GNSO during the development of relevant policies and procedures in accordance with their respective Policy Development Processes (PDPs). These documents also analyze the recommendations and their impact on the gTLD application process, as described in the New gTLD Applicant Guidebook, and on the IDN ccTLD application process, based on the Final Implementation Plan for the IDN ccTLD Fast Track Process. The fundamental premises for the recommendations and analysis presented arise mostly from observations by the community in Integrated Issues Report and advice presented by the Security and Stability Advisory Committee (SSAC) in its SAC 60 report.
While developing the analysis, ICANN organization team has had multiple interactions with the ICANN Board IDN Working Group (BIWG) since 2014, and the BIWG has guided the development of this work. The recommendations have been designed to be conservative, with the view that the IDN variant TLDs are being implemented for the first time, and that the solution could accommodate implementation experience over time.
The ICANN Board notes that the RZ-LGR work is well underway. The ICANN Board also notes that the initial set of recommendations for implementing IDN variant TLDs in a conservative and consistent way are available for further consideration by the ccNSO and GNSO in their work on developing relevant policy and procedures. With the prerequisites identified by the community in Integrated Issues Report in place, the next steps can now be taken by the supporting organizations (SOs).
This will have a positive impact for the community, though there are some associated risks. Minimally, the IDN variant TLDs identified are withheld from application, which will contribute towards the security of the end users, until possibly a feasible management mechanism has been developed by the supporting organizations. Further, if consistent management mechanisms can be agreed by the ccNSO and GNSO on delegating a few of these variant labels, it can help promote the usability of the domain names across the communities which require these IDN variant TLDs. There is risk associated with taking this work forward, especially if a consistent approach to TLDs is not agreed upon by the community, as that can potentially confuse the end-users, or in other cases may cause security issues for them. The IDN variant TLDs can also cause management burden on registrants, as identified by SSAC in its SAC060 report. Following the resolution, ccNSO and GNSO will have to develop their own policies and procedures to implement IDN variant TLDs, taking into account recommendations provided. This resolution, however, is not directing either the ccNSO or the GNSO to undertake policy work on this topic. If and when the respective Final Reports containing policy recommendations, developed through the appropriate PDPs, are submitted to the ICANN Board for approval, the Board will consider how effectively these policies and procedures address the Variant TLD recommendations, their impact and the associated risks. At that time the Board will decide whether to adopt the policy recommendations and move forward with implementing the IDN variant TLDs.
There will eventually be a fiscal impact. The extent of the fiscal impact will depend on the eventual IDN variant TLD application evaluation process and the timing of this application process suggested by the community. Therefore, the impact will need to be gauged when the ccNSO and GNSO finalize their policies and procedures for implementing IDN variant TLDs and present them for consideration by the ICANN Board.
The recommendations contribute towards a secure and stable operation of the unique identifier system, while addressing the need for IDN variant TLDs identified by the community and respecting the community policy development role. The work on IDN variant TLDs also contributes to the public interest by enhancing access to the Internet's Domain Name System (DNS) in different scripts and languages. |
These efforts are led by a cross-disciplinary team focused on finding and disrupting both the most sophisticated influence operations aimed to manipulate public debate as well as high volume inauthentic behaviors like spam and fake engagement. Over the past several years, our team has grown to over 200 people with expertise ranging from open source research, to threat investigations, cyber security, law enforcement and national security, investigative journalism, engineering, product development, data science and academic studies in disinformation.
You can find more information about our previous enforcement actions here.
Purpose of This Report
Over the past three years, we’ve shared our findings about coordinated inauthentic behavior we detect and remove from our platforms. As part of regular CIB reports, we’re sharing information about all networks we take down over the course of a month to make it easier for people to see progress we’re making in one place.
What is CIB?
While we investigate and enforce against any type of inauthentic behavior — including fake engagement, spam and artificial amplification — we approach enforcement against these mostly financially-motivated activities differently from how we counter foreign interference or domestic influence operations. We routinely take down less sophisticated, high-volume inauthentic behaviors like spam and we do not announce these enforcement actions when we take them.
We view influence operations as coordinated efforts to manipulate public debate for a strategic goal where fake accounts are central to the operation. There are two tiers of these activities that we work to stop: 1) coordinated inauthentic behavior in the context of domestic, non-state campaigns (CIB) and 2) coordinated inauthentic behavior on behalf of a foreign or government actor (FGI).
Coordinated Inauthentic Behavior (CIB)
When we find domestic, non-government campaigns that include groups of accounts and Pages seeking to mislead people about who they are and what they are doing while relying on fake accounts, we remove both inauthentic and authentic accounts, Pages and Groups directly involved in this activity.
Foreign or Government Interference (FGI)
If we find any instances of CIB conducted on behalf of a government entity or by a foreign actor, we apply the broadest enforcement measures including the removal of every on-platform property connected to the operation itself and the people and organizations behind it.
We monitor for efforts to re-establish a presence on Facebook by networks we previously removed for CIB. Using both automated and manual detection, we continuously remove accounts and Pages connected to networks we took down in the past.
Summary of April 2020 Findings
This month, we removed eight networks of accounts, Pages and Groups. Two of them — from Russia and Iran — focused internationally (FGI), and the remaining six — in the US, Georgia, Myanmar and Mauritania — targeted domestic audiences in their respective countries (CIB). We have shared information about our findings with law enforcement, policymakers and industry partners.
We know that people looking to mislead others — whether through phishing, scams, or influence operations — try to leverage crises to advance their goals, and the coronavirus pandemic is no different. All of the networks we took down for CIB in April were created before the COVID-19 pandemic began, however, we’ve seen people behind these campaigns opportunistically use coronavirus-related posts among many other topics to build an audience and drive people to their Pages or off-platform sites. The majority of the networks we took down this month were still trying to grow their audience or had a large portion of engagement on their Pages generated by their own accounts.
- Total number of Facebook accounts removed: 732
- Total number of Instagram accounts removed: 162
- Total number of Pages removed: 793
- Total number of Groups removed: 200
Networks Removed in April, 2020:
- Russia: We removed 46 Pages, 91 Facebook accounts, 2 Groups, and 1 Instagram account. This network posted in Russian, English, German, Spanish, French, Hungarian, Serbian, Georgian, Indonesian and Farsi, focusing on a wide range of regions around the world. Our investigation linked this activity to individuals in Russia, the Donbass region in Ukraine and two media organizations in Crimea — NewsFront and SouthFront. We found this network as part of our internal investigation into suspected coordinated inauthentic behavior in the region.
- Iran: We removed 118 Pages, 389 Facebook accounts, 27 Groups, and 6 Instagram accounts. This activity originated in Iran and focused on a wide range of countries globally including Algeria, Bangladesh, Bosnia, Egypt, Ghana, Libya, Mauritania, Morocco, Nigeria, Senegal, Sierra Leone, Somalia, Sudan, Tanzania, Tunisia, the US, UK and Zimbabwe. Our investigation linked this activity to the Islamic Republic of Iran Broadcasting Corporation. We found this network as part of our internal investigations into suspected coordinated inauthentic behavior, based in part on some links to our past takedowns.
- US: We removed 5 Pages, 20 Facebook accounts, and 6 Groups that originated in the US and focused domestically. Our investigation linked this activity to individuals associated with the QAnon network known to spread fringe conspiracy theories. We found this activity as part of our internal investigations into suspected coordinated inauthentic behavior ahead of the 2020 election in the US.
- US: We removed 19 Pages, 15 Facebook accounts, and 1 Group that originated in the US and focused domestically. Our investigation linked this network to VDARE, a website known for posting anti-immigration content, and individuals associated with a similar website The Unz Review. We found this activity as part of our internal investigations into suspected coordinated inauthentic behavior ahead of the 2020 election in the US.
- Mauritania: We removed 11 Pages, 75 Facebook accounts, and 90 Instagram accounts. This network originated in Mauritania and focused on domestic audiences. We detected this operation as a result of our internal investigation into suspected coordinated inauthentic behavior linked to our past takedowns.
- Myanmar: We removed 3 Pages, 18 Facebook accounts, and 1 Group. This domestic-focused network originated in Myanmar. Our investigation linked this activity to members of the Myanmar Police Force. We found this network as part of our internal investigation into suspected coordinated inauthentic behavior in the region.
- Georgia: We removed 511 Pages, 101 Facebook accounts, and 122 Groups, and 56 Instagram accounts. This domestic-focused activity originated in Georgia. Our investigation linked this network to Espersona, a media firm in Georgia. This organization is now banned from our platforms. We found this activity as part of our investigation into suspected coordinated inauthentic behavior publicly reported by a local fact-checking organization in Georgia with some links to our past takedown.
- Georgia: Finally, we removed 23 Facebook accounts, 80 Pages, 41 Groups, and 9 Instagram accounts. This domestic-focused activity originated in Georgia. Our investigation linked this network to individuals associated with United National Movement, a political party. We found this activity as part of our investigation into suspected coordinated inauthentic behavior in the region. Our assessment benefited from local public reporting in Georgia.
We are making progress rooting out this abuse, but as we’ve said before, it’s an ongoing effort. We’re committed to continually improving to stay ahead. That means building better technology, hiring more people and working more closely with law enforcement, security experts and other companies.
See the detailed report for more information.
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As a sponsor of Glamour’s 2017 Women of the Year Summit, we launched a new mentor program in partnership with The Girl Project—Glamour Magazine’s philanthropic initiative. The Girl Project aims to unleash the vast economic and social power of girls through education to ensure that girls everywhere have access to quality secondary education. This mentorship…
In any sport, athletes and amateurs alike are concerned about how their equipment might impact their performance. For gamers, the capabilities of their hardware are fundamental to their experiences. It can be particularly frustrating when the viewing field of the game is interrupted by the bezels of three monitors, or by the slow response of… |
In software security, the idea of Shift Left is the idea of moving security forward in your software development lifecycle (SDLC) into earlier stages of the process to plan security-in before developing the software. It is the software engineering equivalent of ‘measure twice, cut once.’ Depending on where you already are in your software security processes, this could mean different things for each organization.
Shift Left: Application security responsibility and processes can be implemented earlier in the development lifecycle.
Referring to a typical software development lifecycle, as pictured above, shifting left means moving to the left in the diagram and adding more security considerations to earlier parts of the process. For example, many organizations are testing for security vulnerabilities in later stages of the process using scanning tools such as static and dynamic analysis - then penetration testing when running applications are available. The results are then fed back to development to fix problems found, thus creating rework on existing code and taking time away from development. In this simple case, shifting left would involve either scanning during the development phase as code is written or, even better, training developers on security, so the code is more secure in the first place and scans result in fewer findings and less rework. This is a classic first step in shifting left, but far from what is genuinely needed to increase efficiency while also increasing security.
The example above still sees software security as largely a coding and code-testing problem, but truly shifting left involves going all the way left to the beginning of the process. In the earliest phases, where requirements are written, technology is selected, and software is architected, security should play a role even at this stage. Knowledge and skills are required to build-in security from the beginning, such as creating a threat model to understand how you approach risk in the project and which risks are important. This alone can inform the entire project and allow all participants to focus on the critical threats while not wasting time on unimportant threats - making you more secure and efficient at the same time.
The same is true of architecture. Considering security in your architecture while determining details, including how communications will work among components, how data will be stored and managed, how processes will be divided, and the platform technology will significantly reduce risk and security findings that require rework in later stages. When moving onto the design stage, the security considerations given to requirements and architecture, as well as the threat model, will bear fruit in helping ensure the specific designs consider security. Of course, techniques and skills should also be brought to bear on the design. For example, if data security includes encryption, what algorithms will be used, and how will they be implemented?
As you can see, in each stage of the process security can be considered, and the earlier it is, the better off you will be. Of course, if you have been thinking about shifting left for security, you need to train your software teams on what that means and how to do it. This first step is planning what shift left processes you will be doing. You will likely have to take a piece-by-piece approach instead of massively reworking what you are doing in one big push. Changing your processes is a process in itself and will happen over time as you integrate each new concept and review the results obtained.
Start by picking one or two areas to focus on, train the appropriate teams involved in those areas and then implement the plan. For example, if you are already scanning, focus next on training coders on secure coding techniques to prevent rework after scanning. If you have moved down that path and are already training coders, move on to training the team on something that can make the next most significant difference, such as threat modeling. The whole team should get some training here. The senior architects producing the threat model will need the most in-depth training, but others across the cycle will need the training to understand and know how to consume it for their area. Incremental change with training for that change before each step will help build good habits into the process and ensure that you are actually using the training you deploy. Training everyone, everywhere, on everything all at once will likely result in teams being overwhelmed and putting it aside so they can get the software they committed released.
Finally, after all that shift left talk, we also must talk about shifting right. Software security has traditionally been in the middle of the process with coding and testing. As we have moved into a DevOps model, where the software team is responsible for deployment and monitoring, security must also be a consideration at the tail of the process. While IT teams learned about infrastructure security long ago (hopefully), DevOps teams may be coming to the infrastructure they are responsible for without this knowledge. Here too, the approach needs to consider security from the left part of this right side of the process. The DevOps team needs to design secure deployment processes, create secure infrastructure configurations, and plan for monitoring security issues, among other things. Then those plans must be executed. Gaining the knowledge and skills to do this means training on security specific to these roles and risks – and specific to the technology platform being used.
As you can see, shifting left for software security involves a great deal of training outside of just the development role and secure coding. If you are going to shift left, you need to partner with a provider with that content and capability. At Security Innovation, we have been training developers on software security for over a decade and going beyond the code to train everyone involved in the SDLC from the beginning. Shift left isn’t new to us, and our training, labs, and cyber ranges reflect that capability and philosophy. So, if you are considering reducing your risk and increasing your efficiency by shifting left for software security and know you need the training to do it, check us out!
About Fred Pinkett, Senior Director Product Management
Fred Pinkett is the Senior Director of Product Management for Security Innovation. Prior to this role, he was at Absorb, Security Innovation's learning management system partner. In his second stint with the company, he is the first product manager for Security Innovation's computer-based training. Fred has deep experience in security and cloud storage, including time at RSA, Nasuni, Core Security, and several other startups. He holds an MBA from Boston College and a BS in Computer Science from MIT. Working at both Security Innovation and Absorb, Fred clearly can't stay away from the intersection between application security and learning. Connect with him on LinkedIn. |
The Forum of Incident Response and Security Teams (FIRST) holds an annual conference to promote coordination and cooperation among global Computer Security Incident Response Teams (CSIRTs). This year’s conference ran from 26 June to 1 July 2022, in Dublin, Ireland. These are Andrew Cormack’s notes on his own presentation and discussions on automated network/security management at the Academic SIG, #FIRSTCON22.
To help me think about automated systems in network and security management, I’ve put what seem to be the key points into a picture (below). In the middle is my automated network management or security robot. On the left are the systems the robot can observe and control and on the right are its human partner and the things they need.
Taking those in turn, to the left:
- The robot has certain levers it can pull. In network management, those might block traffic flows, throttle, or redirect them; in spam detection they might send a message to the inbox, the spambox, or direct to the bin. The first thing to think about is how those powers could go wrong, now or in the future. In my examples they could delete all mail or block all traffic. If that’s not okay, we need to think about additional measures to prevent it, or at least make it less likely (15).
- Then there’s the question of what data the robot can see, to inform its decisions on how to manipulate the levers. Can it see content, or just traffic data (the former is probably needed for spam; the latter is probably sufficient for at least some network management)? Does it need more or less information, for example, historic data or information from other components of the digital system? If it needs training, where can we obtain that data, and how often does it need updating? (10).
- Finally, we can’t assume that the world the robot is operating in is friendly, or even neutral. Could a malicious actor compromise the robot, or just create real or fake data to make it operate its levers in destructive ways? Why generate a huge DDoS flow if I can persuade an automated network defender to disconnect the organization for me? Can the actor test how the robot responds to changes, and thereby discover non-public information about our operations? Ultimately, an attacker could use their own automation to probe ours (15).
And, having identified how things could go wrong, on the right-hand side:
- What controls does the human partner need to be able to act effectively when unexpected things happen? Do they need to approve the robot’s suggestions before they are implemented (known as human-in-the-loop), or have the option to correct them soon afterwards? If the robot is approaching the limits of its capabilities, does the human need to take over, or enable a simpler algorithm or more detailed logging so that the event can be debugged or reviewed later? (14).
- And, what signals does the human need, to know when and how to operate their controls. This could include individual decisions, capacity warnings or metrics, alerts of unusual situations, and so forth. What logs are needed for subsequent review and debugging (12, 13)?
Having applied these questions to the familiar case of email filtering, they seem to be a helpful guide to achieving the most effective machine/human partnership.
Visualizing the draft EU Artificial Intelligence Act
Encouragingly, my operational thinking about automation seems to come up with a very similar list to the drafters of the EU AI Act. Formally, their requirements will only apply to “high-risk” AI, and it’s not yet clear whether network automation will fall into that category. But it’s always good when two very different starting points reach very similar conclusions — perhaps a useful ‘have I considered…?’ checklist, even if it’s not a legal requirement.
The text can be found below, but I’ve been using this visualization to explain to myself what’s going on. Article numbers are at what I think is the relevant point on the diagram (you may recognize them from the post above). Comments and suggestions very welcome!
What the draft Act says (first sentence of each of the requirement Articles):
- Article 9: A risk management system shall be established, implemented, documented and maintained in relation to high-risk AI systems.
- Article 10: High-risk AI systems which make use of techniques involving the training of models with data shall be developed on the basis of training, validation and testing data sets that meet the quality criteria.
- Article 11: The technical documentation of a high-risk AI system shall be drawn up before that system is placed on the market or put into service and shall be kept up-to date (further detail in Article 18).
- Article 12: High-risk AI systems shall be designed and developed with capabilities enabling the automatic recording of events (‘logs’) while the high-risk AI systems is operating. Those logging capabilities shall conform to recognized standards or common specifications (obligations on retention in Article 20).
- Article 13: High-risk AI systems shall be designed and developed in such a way to ensure that their operation is sufficiently transparent to enable users to interpret the system’s output and use it appropriately.
- Article 14: High-risk AI systems shall be designed and developed in such a way, including with appropriate human-machine interface tools, that they can be effectively overseen by natural persons during the period in which the AI system is in use.
- Article 15: High-risk AI systems shall be designed and developed in such a way that they achieve, in the light of their intended purpose, an appropriate level of accuracy, robustness and cybersecurity, and perform consistently in those respects throughout their lifecycle.
- Article 17: Providers of high-risk AI systems shall put a quality management system in place that ensures compliance with this Regulation.
- Article 19: Providers of high-risk AI systems shall ensure that their systems undergo the relevant conformity assessment procedure in accordance with Article 43, prior to their placing on the market or putting into service.
- Article 29: Users of high-risk AI systems shall use such systems in accordance with the instructions of use accompanying the systems.
Before FIRSTCON2022, I had a fascinating chat with a long-standing friend/colleague Aaron Kaplan who knows far more about incident response technology than I ever did. The conclusions we reached were that using machine learning (ML) or AI in cyber defence will be a gradual journey that should benefit defenders at least as much as AI does attackers.
On the defender side, we can become more efficient by using ML decision support tools to free up analysts’ and incident responders’ time to do the sort of things that humans will always be best at, while exploring what aspects of active defences can be automated.
Of course, attackers will also get new tools too, but will likely lean toward mass attacks that are noisy. One of the few things I’ve always taken reassurance from is that a mass attack is easily detectable simply because it is mass. It might take us a while to work out what it is, but so long as we share information, that doesn’t seem impossible. That’s how spam detection continues to work, and I’d settle for that level of prevention for other types of attack!
Some organizations will, by their nature, be specific targets of particularly well-funded attackers who may use ML for precision. Those organizations need equivalent skills in their defenders. But for most of us our defences need to be good — say, a bit better than good practice — but probably not elite.
Andrew Cormack is Chief Regulatory Advisor at Jisc, and is responsible for keeping an eye out for places where our ideas, services and products might raise regulatory issues. Andrew ran the JANET-CERT and EuroCERT Incident Response Teams.
This post is adapted from posts at Jisc Blog.
The views expressed by the authors of this blog are their own and do not necessarily reflect the views of APNIC. Please note a Code of Conduct applies to this blog. |
Sentinella is a desktop application that monitors your system activity and, when a condition is met, takes the action that you've chosen. While monitoring your CPU, memory, hard drive, and network usage, Sentinella can be programmed to take specific actions when setpoints for utilization or time are met. It can power off, reboot, or hibernate your system, kill an active process, throw an alarm, or execute any command. Sentinella integrates perfectly with the main desktop enviroments (KDE, GNOME, XFCE, and others) and works under many Unix systems.
libsysactivity is a lightweight library that retrieves statistics of the system's activity in a portable and thread safe way. In each OS that it supports, it offers the same API for retrieving the activity of Hard disks, CPUs memory, Processes, Network interfaces, Memory, and Swaps. |
Feint Definition - MilitaryDictionary.org
Term Source: JP 3-13.4 (Military Deception) ?
1.) In military deception, an offensive action involving contact with the adversary conducted for the purpose of deceiving the adversary as to the location and/or time of the actual main offensive action.
In the United States, military vocabulary is standardized by the Department of Defence. These terms are used by the United States Army, Navy, Air Force, and Marine Corps.
Term Classification: operations
Department of Defence, Dictionary of Military and Associated Terms
Term sourced from JP 3-13.4: Military Deception, updated January 2012
This term is marked as active and was last updated in 2015
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Media access control is a sub layer of data link layer of OSI reference model. Media access control sub layer is considered to be the layer 2 in data link layer. Media access control uses some sets of rules that govern the communications in the network and it is the interface between the logical link control layer and the physical layer. This means that media access control communicates with both logical link layer and the physical layer. In short media access control provides a physical way of communication.
Media access control also provides channel accessing mechanism and addressing techniques which is the unique identifier that has been assigned to the network.
Media access control |
by Markus Hittmeir, Andreas Ekelhart and Rudolf Mayer (SBA Research)
The generation of synthetic data is widely considered to be an effective way of ensuring privacy and reducing the risk of disclosing sensitive information in micro-data. We analysed these risks and the utility of synthetic data for machine learning tasks. Our results demonstrate the suitability of this approach for privacy-preserving data publishing.
Recent technological advances have led to an increase in the collection and storage of large amounts of data. Micro-data, i.e. data that contains information at the level of individual respondents, is collected in domains such as healthcare, employment and social media. Its release and distribution, however, bears the risk of compromising the confidentiality of sensitive information and the privacy of affected individuals. To comply with ethical and legal standards, such as the EU's General Directive on Data Protection (GDPR), data holders and data providers have to take measures to prevent attackers from acquiring sensitive information from the released data.
Traditional approaches to compliance often include anonymisation of data before publishing or processing, such as using k-anonymity or differential privacy. Synthetic data offers an alternative solution. The process of generating synthetic data, i.e. data synthetisation, generally comprises the following steps:
- Data description: The original data is used to build a model comprising information about the distribution of attributes and correlations between them.
- Data generation: This model is then used to generate data samples. The global properties of the resulting synthetic dataset are similar to the original, but the samples do not represent real individuals.
The goal of this technique is that analysis methods trained on the synthetic instead of the real data do not perform (notably) worse. The use of synthetic data should also reduce the risk of disclosure of sensitive information, as the artificially generated records do not relate to individuals in the original data in a one-to-one correspondence. Consequently, validating the utility and privacy aspects is crucial for trust in this method. We conducted an empirical evaluation, including three open-source solutions: the SyntheticDataVault (SDV) [L1], DataSynthesizer (DS) [L2] and synthpop (SP) [L3]. The SyntheticDataVault builds a model based on estimates of the distributions of each column. Correlations between attributes are learned from the covariance matrix of the original data. The model of the DataSynthesizer is based on a Bayesian network and uses the framework of differential privacy. Finally, synthpop uses a classification and regression tree (CART) in its standard settings.
The utility of the generated synthetic data can be assessed by evaluating the effectiveness of machine learning tasks. Models that are trained on the synthetic data can be compared with models trained on the original data, and scored on criteria such as accuracy and F-score for classification problems. We studied classification and regression tasks on publicly available benchmark datasets. While the results vary depending on the number of attributes, the size of the dataset and the task itself, we can identify several trends. In general, models based on synthetic data can reach utility up to or very close to the original data. Models trained on data from the DataSynthesizer without Differential Privacy or on data from synthpop with standard settings tend to achieve utility scores that are close to those of the model trained on the original data.
On the other hand, the SyntheticDataVault seems to produce data with larger differences to the original, which usually leads to reduced effectiveness. The same is true for the DataSynthesizer when Differential Privacy is enabled. These trends also manifest in direct comparisons of the datasets’ properties, e.g., in the heatmaps of pairwise correlations shown in Figure 1.
Figure 1: Heatmaps for SyntheticDataVault and DataSynthesizer on the Adult Census Income dataset [L4].
A basic assumption is that privacy is endangered if the artificial rows in synthetic data are very close or equal to the rows of actual individuals in the original data. Privacy risks could therefore by assessed by computing the distance between each synthetic sample and the most similar original record. Visualisations of these minimal distances can be seen in Figure 2 (the x-axis shows the distance, the y-axis counts the number of records). While the DataSynthesizer without Differential Privacy leads to many records with small distances to original samples, the SyntheticDataVault generates much larger differences.
Figure 2: Distance Plots for SyntheticDataVault and DataSynthesizer on the Adult Census Income dataset.
We complemented this privacy analysis on synthetic data by establishing a baseline for attribute disclosure risks . Attribute disclosure happens when an attacker knows the values of quasi-identifying attributes of their victim (such as birth date, gender or ZIP), and is able to use some data source to infer the value of sensitive attributes (such as personal health data). By considering several scenarios on benchmark datasets, we demonstrated how an attacker might use synthetic datasets for the prediction of sensitive attributes. The attacker’s predictive accuracy was usually better for the DataSynthesizer without Differential Privacy and for synthpop than it was for the SyntheticDataVault. However, both the amount of near-matches in the analysis of Figure 2 and the computed attribute disclosure scores show that the risk of reidentification on synthetic data is reduced.
Our evaluations demonstrate that the utility of synthetic data may be kept at a high level and that this approach is appropriate for privacy-preserving data publishing. However, it is important to note that there is a trade-off between the level of the utility and the privacy these tools achieve. If privacy is the main concern, we recommend that samples are generated based on models that preserve fewer correlations. This reduces the attribute disclosure risk and ensures that the artificial records are not too similar to the originals.
On the utility of synthetic data: An empirical evaluation on machine learning tasks, ARES ‘19 Proc., Canterbury, UK, https://doi.org/10.1145/3339252.3339281
Utility and privacy assessments of synthetic data for regression tasks, IEEE BigData '19 Proc., Los Angeles, CA, USA, https://doi.org/10.1109/BigData47090.2019.9005476
A baseline for attribute disclosure risk in synthetic data, CODASPY '20 Proc., New Orleans, LA, USA, https://doi.org/10.1145/3374664.3375722
Markus Hittmeir, Andreas Ekelhart, Rudolf Mayer, SBA Research, Austria |
FREQUENCY-BASED FEATURE EXTRACTION FOR MALWARE CLASSIFICATION
Erwert, Jonathan P.
Rowe, Neil C.
MetadataShow full item record
Traditional signature-based malware detection is effective, but it can only identify known malicious programs. This thesis attempts to use machine-learning techniques to successfully identify previously unknown malware from a set of Windows executable programs. We analyzed the histogram of 4-, 8-, and 16-bit-sequence values contained in each program. We then analyzed the effectiveness of using these histograms in part or in full as feature vectors for machine learning experiments. We also explored the effect of an offset at the beginning of each program and its impact on classifier performance. We successfully show that a machine learning classifier can be learned from these features, with an f-measure in excess of 90% attained in one of our experiments. Using a part of the histogram as the feature vector did not significantly affect classifier performance up to a point, nor did including an offset. Our results also suggest that features derived from histograms are better suited to tree-based algorithms compared to Bayesian methods.
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
Showing items related by title, author, creator and subject.
Alves, Jorge; Herman, Jessica; Rowe, Neil C. (Monterey, California. Naval Postgraduate School, 2004-06);Accurate identification of unknown contacts crucial in military intelligence. Automated systems that quickly and accurately determine the identity of a contact could be a benefit in backing up electronic-signals ...
Richstein, James K. (Monterey, California. Naval Postgraduate School, 1993-12);Histogram generation, a standard image processing operation, is a record of the intensity distribution in the image. Histogram generation has straight forward implementations on digital computers using high level languages. ...
Borges, C.F. (1999);We examine the histogram method proposed in for estimating the parameters associated with a Markov random field. This method relies on the estimation of the local interaction sums from histogram data. We derive an ... |
Software Reverse-Engineering (5-day)
Reverse-engineering is an essential skill for many cybersecurity disciplines - vulnerability assessment, malware analysis, and software interoperability.
This course is designed to introduce students to the fundamentals of reverse-engineering software. These fundamentals are common to desktop, mobile, and embedded architectures.
Over five days, we introduce students to the x86 instruction set and CPU architecture, recognizing C code constructs in assembly code, reverse-engineering with IDA Pro, and binary vulnerability research. Lectures will be supported by extensive supervised lab exercises that will reinforce and cement knowledge.
After taking this course, students will be proficient in the fundamentals of reverse-engineering software using IDA Pro, without the help of source code or documentation.
Boston Cybernetics Institute 2021©️ |
The default logging mode is delaylog. In delaylog mode, the effects of most system calls other than
pwrite(2) are guaranteed to be persistent approximately 3 seconds after the system call returns to the application. Contrast this with the behavior of most other file systems in which most system calls are not persistent until approximately 30 seconds or more after the call has returned. Fast file system recovery works with this mode.
rename(2) system call flushes the source file to disk to guarantee the persistence of the file data before renaming it. In the log and delaylog modes, the rename is also guaranteed to be persistent when the system call returns. This benefits shell scripts and programs that try to update a file atomically by writing the new file contents to a temporary file and then renaming it on top of the target file. |
The Honor Ransomware is a file-encryption Trojan, which is likely to still be in development since its current state does not allow it to deploy a ransom note to the attacked computer. This means that any user whose files gets taken hostage by the Honor Ransomware will not receive any recovery instructions so that they would not be able to co-operate with the attackers even if they wanted to. When there is ransomware involved, there is a significant chance that you will not be able to fully recover your files unless you have a safe backup to restore the data from.
Unfortunately, the unfinished version of the Honor Ransomware is still able to cause significant damage to a long list of file formats – documents, spreadsheets, archives, media, etc. Whenever a file is encrypted successfully, the Honor Ransomware will scramble its name, and then append the ‘.honor’ extension to it. This might make the situation of the victims even more distressful since they will not be able to identify quickly how many of their important files were taken hostage.
Since the Honor Ransomware is not yet complete, it is unlikely that its authors have started distributing it yet. However, when this happens, we expect to see the Honor Ransomware being spread via spear-phishing e-mails, which either contain a corrupted attachment or tricks the users into downloading a suspicious file hosted on an online location.
Remember that the correct way to fight ransomware and its authors is never agreed to pay the ransom sum they demand. Although the Honor Ransomware does not provide a ransom message, you can rest assured that its authors are encrypting the files of their victims just so that they can use them to extort money from the victim. Unfortunately, many file-encryption Trojans use very secure methods to lock the files of their victims so that the free recovery of the data might be nearly impossible. Sometimes 3rd-party file recovery utilities might be able to get some files back, but making a full recovery is unlikely. If you have a hunch that your computer was attacked by the Honor Ransomware, then we suggest running a reputable anti-malware scanner, which will eradicate the threat’s files. |
Zapraszamy na pażdziernikowe spotkanie (ISC)2 Poland Chapter.
Poniżej znajduje się opis wydarzenia:
Alex Vaystikh: Machine Learning: Does it help or hurt cybersecurity?
* Machine learning anomaly detection has been hyped as the answer to increasingly ineffective signature AV solutions. We would argue that machine learning could make a security analyst’s job more difficult, and at times, impair the level of cybersecurity.
* Supervised machine learning is often used in file analysis such as endpoint and anti-virus solutions – where it can be of an advantage.
* What happens when supervised machine learning is used in highly dynamic use cases like network traffic analysis
* What is Unsupervised machine learning and how it’s differentiated from supervised machine learning
* What unsupervised machine learning can offer in network traffic analysis
Place: Ernst and Young
Date: 25.10.2018 18:00
Registration link: https://isc2polandchapter-machinelearning.evenea.pl/ |
Data Loss Prevention Policy Template
Today, data is more available, transferable and sensitive than ever. The best way to stop data leaks is to implement a Data Loss Prevention (DLP) solution. DLP enforces an automated corporate policy, which can identify and protect data before it exits your organization
Many tools, including dedicated DLP tools, email servers and general purpose security solutions, offer data loss prevention policy templates. These templates can help you easily create DLP policies that define which organizational content should be protected by a data loss policy. For example, DLP can ensure content identified by the policy is not transmitted to external individuals, modified or deleted.
In this post you will learn:
- What is a data loss prevention policy?
- Why it is important to have a data loss prevention policy
- Best practices for creating a successful DLP policy
- Data loss prevention templates for common enterprise tools:
What is a data loss prevention policy?
Data loss prevention (DLP) safeguards the information of an organization and stops end -users from leaking sensitive data outside the network. Network administrators use DLP tools to track data shared and accessed by end users. DLP tools can protect and classify data, while data loss prevention policies outline how organizations should implement these tools.
DLP software classifies the confidential and essential data of an organization. The software isolates violations of policies, as defined by a predefined policy pack or by the organization. Regulatory compliances such as PCI-DSS, HIPAA or GDPR generally shape these policies. Once the software identifies violations, DLP imposes remediation with encryption, alerts and other measures to stop end users from inadvertently or maliciously exposing the data.
Data loss prevention tools scan endpoint activities, and monitor data in the cloud to safeguard data at rest, in use and in motion. They also filter data streams on organizational networks. An organization can use DLP reporting functions to ensure they adhere to auditing and compliance requirements and to isolate abnormal activity and areas of weakness in their organization. This assists with incident response and forensics.
Why it is important to have a data loss prevention policy
Data security prevents hostile attacks on an organization. Employees have many ways to share and access distributed organization data, making inadvertent data loss a pressing issue.
Employees, business partners, and contractors can also pose a threat to the organization when they steal or accidentally leak company data. Employees may, for example, fall victim to social engineering attacks which highlights the need for ongoing employee cyber education. These kind of threats or insider threats present a large risk to businesses today.
Data storage is now more accessible via remote locations and in cloud services, individuals with ill intent can access the data from poorly protected phones and laptops.
There are three key reasons for having a data loss prevention policy:
Organization policies are guided by mandatory compliance standards specified by governments and industry regulators (such as SOX, HIPAA, PCI DSS). These standards outline how an organization should safeguard personally identifiable information (PII), and other sensitive data.
A DLP policy is the first stage in compliance and helps provide accurate reporting for audits. Typically DLP tools are designed for the requirements of common standards for a particular industry.
2. Intellectual property
Trade secrets or other intangible assets, including organizational strategies and customer lists, might be of greater value than physical assets. Losing this kind of information may create financial and reputational damage, misappropriation, and can result in penalties and legal action.
3. Data visibility
With the growing movement towards digitization, sensitive information is found on devices such as servers, laptops, network shares, cloud storage, databases, and USB drives.
A DLP policy can help organizations learn how stakeholders and end users use sensitive information. An organization can better safeguard its information, when it has visibility over what data exists, where it resides, who uses it, and for what purpose.
Best practices for creating a successful DLP policy
Although no protection is absolute, best practices can help your organization implement a successful data protection policy.
- Identify data that requires protection—see which information requires protection, by classifying, prioritizing, and interpreting data based on its vulnerability and risk factors.
- Understand how to assess vendors—establish a framework with relevant questions to make an informed purchasing decision.
- Specify the roles of all parties involved—outline the role of every individual to prevent data misuse.
- Monitoring data movement—understand how data is used and identify behavior that puts data at risk. Use this knowledge to develop policies that mitigate the risk of data loss and ensure appropriate data use.
- Involve leadership—management buy-in is crucial to the success of DLP. Policies are not worth anything unless they can be applied at an organizational level. Department heads should create a data loss prevention policy that is in keeping with corporate culture.
- Educate the workforce—we tend to view employees as the weak link in data loss prevention, yet executives often don’t prioritize education. Invest is helping users of data and stakeholders understand the policy and its importance.
- Use metrics to determine success—measure DLP success using metrics, including the number of incidents, percentage of false positive, and average time to respond. Data loss prevention metrics will help you see how efficient your policy is, and the return on your investments.
Data loss prevention templates
Data loss prevention policy templates use DLP data identifiers and logical operations (And, Or, Except) to create condition statements. Only data or files that meet a certain condition statement will fall within the confines of a DLP policy.
For example, a DLP policy can specify that a file belongs to the sensitive “employment contracts” category if it meets all of the following criteria:
- Must be a Microsoft Word file (file attribute)
- AND must contain certain legal terms (keywords)
- AND must contain ID numbers (defined by regular expression)
DLP policies on Microsoft Exchange
Microsoft Exchange offers data loss prevention (DLP) policy templates that can help safeguard organizational data stored and transmitted via an Exchange server.
They can help you manage payment card industry data security standard (PCI-DSS), Gramm-Leach-Bliley act (GLBA) data, and United States personally identifiable information (U.S. PII). DLP policies help with the full scope of traditional mail flow rules, and you can add more rules after establishing a DLP policy.
Prerequisites for creating Microsoft Exchange DLP templates:
- Set up the exchange server – see this TechNet article for details.
- Configure the user and administrator accounts and check the transport pipeline (to ensure you can send email to external email clients). For more details read the document here.
- Receive permission from the security team or relevant authorities to create a DLP policy.
- DLP requires an Exchange Enterprise Client Access License (CAL).
- In hybrid environments where certain mailboxes are in on-premises Exchange and some are in Exchange Online, DLP policies are only applied in Exchange Online.
Examples of available DLP templates in Exchange:
|Policy template||Examples of information the template is used to detect and protect|
|PCI Data Security Standard (PCI DSS)||Debit card or credit card numbers|
|U.K. Data Protection Act||National insurance numbers|
|U.S. Health Insurance Act (HIPAA)||Social security numbers and health information|
|Portability and Accountability Act (HIPAA)||U.S. Personally Identifiable Information (PII), for example, social security numbers or driver’s license numbers.|
|France Data Protection Act||Health insurance card number|
|Canada Personal Information Protection Act (PIPA)||Passport numbers and health information|
|Australia Privacy Act||Financial data in Australia, including credit cards, and SWIFT codes|
|Japan Personally Identifiable Information (PII) Data||Driver’s license and passport numbers|
See all templates provided by Exchange server.
How to create a DLP policy from a template using the Exchange Admin Center (EAC):
1. In the EAC, navigate to Compliance Management > Data Loss Prevention, then click Add.
2. The create a New DLP Policy from a Template page appears. Fill in the policy name, description, select the template, and set a status—whether you want to enable the policy or not. The default status is Test Without Notifications.
3. Click Save.
DLP policies in Symantec Data Loss Prevention
Symantec Data Loss Prevention offers policy templates you can use to safeguard organizational data. You can import and export policy rules and exceptions as templates by sharing policies across environments and systems.
|Policy template||Selected example||Example description|
|US Regulatory Enforcement||HIPAA and HITECH (including PHI)||Enforces the US Health Insurance Portability and Accountability Act (HIPAA)|
|General Data Protection Regulation||General Data Protection Regulation (Digital Identity)||Protects personal identifiable information connected to digital identity|
|International Regulatory Enforcement||Caldicott Report||Protects UK patient information|
|Customer and Employee Data Protection||Employee Data Protection||Detects employee data|
|Confidential or Classified Data Protection||Encrypted Data||Detects the use of encryption using different methods|
|Network Security Enforcement||Password Files||Detects password file formats|
|Acceptable Use Enforcement||Restricted Files||Detects file types that may be inappropriate to send out of the company|
|Policy template import and export||Policy template import and export||You can import and export policy templates to and from the Enforce Server. You can share policy templates across environments, archive legacy policies, and version existing policies.|
See all Symantec DLP templates here, organized into the categories above.
To create a DLP policy from a template in Symantec Data Loss Prevention:
- Add a policy from a template. See this help article.
- Choose the template you want to use. The Manage > Policies > Policy List > New Policy – Template List screen lists all policy templates.
- Click Next to configure the policy.
- Choose a Data Profile (if prompted), edit the policy name or description (optional), select a policy group (if necessary), edit the policy rules or exceptions (if necessary).
- Save the policy and export it.
DLP policies in IBM Endpoint Manager (IBM BigFix)
IBM Endpoint Manager, renamed IBM BigFix, is an end-to-end security solution for endpoints which also covers Data Loss Prevention. IBM BigFix’s Core Protection Module (CPM) provides predefined templates:
- GLBA: Gramm-Leach-Billey Act
- SB-1386: US Senate Bill 1386
- HIPAA: Health Insurance Portability and Accountability Act
- PCI-DSS: Payment Card Industry Data Security Standard
- US PII: United States Personally Identifiable Information
Templates are provided as XML files, which you can import to apply the template. BigFix also lets you create your own templates, once you configure DLP data identifiers.
How to import and use a pre-built DLP template in IBM BigFix:
- Navigate to Endpoint Protection > Configurations > Data Protection > DLP Settings Wizard > Template Management.
- On the new screen display type a name for the template, a description, and select data identifiers.
You can add new expressions to search content you want to allow or disallow, create a list of file attributes, and create a keyword list. Each definition should have a logical operator.
- Click Save.
For more details see this support article from IBM.
Complementing DLP with advanced security analytics
DLP solutions are can monitor data flows and secure organizations against known threats. However, attacks and malicious insiders constantly find new ways to compromise systems and steal data, many of which cannot be captured by DLP policy rules. This can be solved by a new type of security tool called User and Event Behavioral Analytics (UEBA).
UEBA tools establish baselines for the behavior of users, applications and network devices. They use machine learning algorithms to identify abnormal activity for an entity or group of entities, without having any predetermined rules or patterns. This complements DLP by alerting about data-related incidents that did not match any DLP policy rule.
For an example of a UEBA system that can protect against data breaches from insider or unknown threats, learn more about Exabeam Advanced Analytics.
Want to learn more about DLP?
Have a look at these articles: |
blocking subdomains with Deco P9
I use the Deco app to set up parental controls. If I block a domain like example-dot-com, will it block all sub-domains such as node1.example-dot-com and node2.example-dot-com? (I took out the dot because it wouldn't let me post this.)
Does Deco block the DNS request, the IP address, or by inspecting HTTP headers?
If IP, how does it work with multiple IPs per hostname?
If it blocks at DNS level, how does it work with DoH (DNS over HTTP)?
Can the parental control be thwarted by modern encryption of the HTTPS. I'm not an expert, but it looks like Encrypted Client Hello (ECH) encrypts the hostname in the HTTP request. |
A Framework for LLVM Code VerificationThe Vienna Verification Toolkit (VVT for short) is a collection of programs and libraries for program verification. The programs of the toolkit include:
- A tool to encode an LLVM program into a transition relation which can then be processed by the rest of the tools called vvt-enc
- By using different techniques such as program slicing, constant propagation and expression simplification vvt-opt can be used to optimize a transition relation.
- vvt-verify is used to verify that a given transition relation contains no bugs using a combination of IC3 and predicate abstraction.
- To quickly find bugs in a transition relation, vvt-bmc employs bounded model checking to uncover them. |
The federal government and military sectors are continuously under cyber attack by sophisticated and nation-state threat actors. New threats are discovered every day, which means your organization needs the most advanced detection and response capabilities available as well as custom cybersecurity solutions.
Governments have a unique set of challenges that necessitates unique solutions. They are not looking for complicated training cycles or complex installations. Working with legacy systems is virtually a prerequisite. Operational disruptions can be devastating.
Cyber deception offers solutions for all of these challenges. With deception technology, governments can match the sophisticated tactics of nation-state threat actors. We have seen our technology do just that for governments across the world. Download our federal fact sheet to find out exactly how governments are using CounterCraft.
Read on to find out the top three use cases governments have for cyber deception.
1. Offensive and Defensive Cyber Operation Capabilities
Nation-state level threats require active defense. Cybersecurity teams need tools to be able to interact with different ransomware operators, phishing, cyber criminals they have been investigating. Goals are tough, ranging from getting attribution to evidence gathering for asset forfeiture or seizure.
Using The Platform™, cyber operations teams and program managers have the ability to utilize best-in-class technology to collect and analyze tactics, techniques, and procedures (TTPs) from adversaries and insider threats while supporting threat hunting, red teaming, and more. The Platform™ features quick deployment to support OCO and DCO missions.
2. Cyber Crime Intel for Analysts and Investigators
Deception provides a vehicle to collect adversary generated threat intelligence. This is threat intelligence generated by an actual adversary targeting your specific organization/network/infrastructure, how they are attacking it, with what tools and techniques, and outlining their objectives.
CounterCraft’s deception-based technology provides security teams with access to threat intelligence and critical cybercrime information including attribution, tactics and other mission-critical data, allowing for faster mitigation of threats posed by cybercrime actors.
3. Cyber Threat Detection for Sensitive and High-Value Systems
Our deception-based technology specializes in detecting and diverting lateral movement and insider threats in real-time that traditional and less sophisticated cybersecurity detection and response solutions will miss.
We make it easy to set up deception environments in unused network environments to identify actors targeting that organization or sector. Capitalizing on these networks to set up deception environments and gather actionable intel on adversaries can be used to indict and fortify the federal sector’s networks.
Our clients use our technology successfully every day to stay one step ahead of cyber criminals.
Learn how active defense works as a force multiplier. Born from the GCHQ Accelerator, and backed by In-Q-Tel, our technology is tested by the Defense Innovation Unit and trusted by the US Air Force. Request a demo today!
Download our fact sheet to learn more about how governments across the world use CounterCraft as part of their cybersecurity strategy.
Dan Brett is the Chief Product Officer and co-founder of CounterCraft. Highly accomplished in achieving outstanding growth for B2B startups, he contributes a great depth of cybersecurity knowledge and understanding of consumer behavior. Follow him on LinkedIn. |
More often, I tend to see correlations between my business world and things in everyday life. To be fair, cyber security – the main focus of my business – is part of all of our lives on almost a daily basis. Whether it’s news about the latest threat or more advice on how to protect ourselves, it is a topic that is prominent. The correlations are more around how elements of cyber security can be relatable to others. Though we hear about cyber security often, it can be difficult to understand the nuances.
The latest correlation I experienced was in relation to basic access control mechanisms called blacklisting and whitelisting. In blacklisting, everyone has access except for members on the blacklist of who are denied access. On the contrast, with whitelisting everyone is denied except for the members on the whitelist.
How does this relate to everyday life? I was in the airport and heading towards security when I thought about the blacklist/whitelist correlation. Most of us are certainly familiar with the security line. We stand in line waiting for our credentials to be checked to see if we are on the no-fly list (aka the blacklist). If we are, access to boarding the plan is denied. On the other hand, some people all but bypass the long wait in the security lines as they have access to the TSA pre-check or CLEAR lines. This is due to the fact that they have already gone through the process of getting pre-qualified to fly (aka the whitelist).
Both methods of security checks are valid and have a purpose to keep us all safer as we fly. When it comes to cyber security, both methods also have a place to help protect the industrial control systems and the assets they control. The most important element is knowing when to apply the techniques.
Applying blacklisting techniques is quite common. Antivirus and anti-malware software is used to block bad actors. However application whitelisting is a more nuanced method that requires collaboration with security solution vendors to calibrate deployments based on baseline settings. This upfront time commitment results in a stronger layer of protection for servers from malware and zero-day attacks.
Learn more about how application whitelisting is an effective strategy in any cyber security program in a recent article published in Control Engineering here. |
Zero Trust is a modern security model founded on the design principle “Never trust, always verify.”
It requires all devices and users to be authenticated, authorized, and regularly validated before being granted access, regardless of whether they are inside or outside an organization's network.
In short, Zero Trust says “Don’t trust anyone until they’ve been verified.”
Zero Trust helps prevent security breaches by eliminating the implicit trust from your system’s architecture. Instead of automatically trusting users inside the network, Zero Trust requires validation at every access point. It protects modern network environments using a multi-layered approach, including:
- Network segmentation
- Layer 7 threat prevention
- Simplified granular user-access control
- Comprehensive security monitoring
- Security system automation
With the rise of remote work, bring your own device (BYOD), and cloud-based assets that aren’t located within an enterprise-owned network boundary, traditional perimeter security falls short. That’s where Zero Trust comes in.
In essence, Zero Trust security acknowledges that threats exist inside and outside of the network and assumes that a breach is inevitable (or has likely already occurred). As a result, it constantly monitors for malicious activity and limits user access to only what is required to do the job. This effectively prevents users (including potential bad actors) from moving laterally through the network and accessing any data that hasn’t been limited.
What Is Zero Trust?
If a relationship between two people devolves to the point of zero trust, it may be time to move on, or at least buy a safe. In the world of information technology and security, a zero-trust relationship is more complicated and the negative consequences could be far more damaging.
From an IT security architecture perspective, the essence of zero trust assumes that no user or asset can be implicitly trusted. ZTA assumes that attackers are already inside your environment and pillaging at will.
Everything any user, application, or device attempts to do or change within a ZTA environment must be continually verified as authentic and authorized for execution.
Zero Trust was coined by a Forrester analyst in 2010, and Google moved the term along during the next few years to enable protected computing by remote workers without using a virtual private network (VPN). The framework was codified in 2018 when NIST issued Special Publication 800-207, Zero Trust Architecture. Core components were updated by NIST in 2020.
Forrester and Gartner continued evolving their ZTA models, and in 2021, Microsoft’s Zero Trust Adoption Report documented major traction by 96 percent of 1,200 security decision-makers who stated that zero trust was critical to their organization’s success.
Adopting a Zero Trust Architecture
As an architecture focused on trust, it’s not surprising that the original concept of ZTA was grounded in identity and access management (IAM).
Gartner defines IAM as “multiple technologies and business processes to help the right people or machines to access the right assets at the right time for the right reasons while keeping unauthorized access and fraud at bay.”
On the surface, Gartner’s definition almost sounds like ZTA. The intent is identical, without a doubt. But doubt we must, for ZTA entails a far broader range of integrated controls required to enable a trusted ecosystem. Since concepts related to ZTA emerged thirteen years ago, analysts, security architects, standards organizations, security and IT suppliers, and enterprise security practitioners have pondered, researched, developed, trialed, and road-tested what a ZTA ecosystem entails. The conclusion: ZTA extends far beyond only IAM.
The Qualys GovCloud Platform is the most advanced security platform for federal, state, and local agencies, as well as regulated private sector firms that need highly secure zero-trust hybrid IT infrastructures that comply with the Zero Trust Security Model and broader mandates for guidelines in NIST Special Publication 800-53 v5.
The Qualys platform is built with the world’s most comprehensive Vulnerability Management (VM) capabilities, including its own asset inventory, threat database, and attack surface management. The apps required for ZTA compliance are delivered via one platform, managed with one dashboard, and deployed with a single agent.
By using the Qualys Cloud Platform, organizations can simplify and achieve compliance across a broad range of ZTA requirements with integrated security and compliance solutions, one centralized control center, and a single agent.
Whether your organization is a federal agency, supplier, or civilian enterprise, we encourage you to learn more about the Qualys Cloud Platform and how it can help your organization comply with national policy for cybersecurity by effectively implementing a zero-trust architecture and model. |
Secure Data Transmission in MANET Routing Protocol
Information exchange in a network of mobile and wireless nodes without any infrastructure support such networks are called as Ad-hoc networks. A Mobile Ad hoc NETwork (MANET) is mobile, multi-hop, infrastructure less wireless network which is capable of autonomous operation. In this paper the authors have discussed some of the basic routing protocols in MANET like Destination Sequenced Distance Vector(DSDV), Dynamic Source Routing(DSR), Ad-hoc On Demand Distance Vector(AODV) and Zone Routing Protocol(ZRP). Security is one of the biggest issue in MANETs as they are infrastructure-less and autonomous. Therefore, in MANET networks with security needs, there must be two considerations kept in mind: one to make the routing protocol secure and second one to protect the data transmission. |
On the Web Application Firewall > Rules page, click Add Rule Chain to add a new rule chain. To edit an existing rule chain, click its Edit Rule Chain icon under Configure.
The New Rule Chain screen or the screen for the existing rule chain displays. Both screens have the same configurable fields in the Rule Chain section.
On the New Rule Chain page, type a descriptive name for the rule chain in the Name field.
Select a threat level from the Severity drop-down menu. You can select HIGH, MEDIUM, or LOW.
Select Disabled, Detect Only, or Prevent from the Action drop-down menu.
Disabled – The rule chain should not take effect.
Detect Only – Allow the traffic but log it.
Prevent – Block traffic that matches the rule and log it.
The Disabled option allows you to temporarily deactivate a rule chain without deleting its configuration.
In the Description field, type a short description of what the rule chain matches or other information.
Select a category for this threat type from the Category drop-down menu. This field is for informational purposes and does not change the way the rule chain is applied.
Under Counter Settings, to enable tracking the rate at which the rule chain is being matched and to configure rate limiting, select Enable Hit Counters. Additional fields are displayed.
In the Max Allowed Hits field, enter the number of matches for this rule chain that must occur before the selected action is triggered.
In the Reset Hit Counter Period field, enter the number of seconds allowed to reach the Max Allowed Hits number. If Max Allowed Hits is not reached within this period, the selected action is not triggered, and the hits counter is reset to zero.
Select Track Per Remote Address to enforce rate limiting against rule chain matches coming from the same IP address. Tracking per remote address uses the remote address as seen by the SMA appliance. This covers the case where different clients sit behind a firewall with NAT enabled, causing them to effectively send packets with the same source IP.
Select Track Per Session to enable rate limiting based on an attacker’s browser session. This method sets a cookie for each browser session. Tracking by user session is not as effective as tracking by remote IP if the attacker initiates a new user session for each attack.
Click Accept to save the rule chain. A Rule Chain ID is automatically generated. |
What Does CRUD Mean?
In the world of cybersecurity, the term CRUD holds significant importance. CRUD, which stands for Create, Read, Update, and Delete, represents the basic functions of persistent storage in computer systems. But what does CRUD mean in the context of cybersecurity, and why is it essential to understand its implications?
In this article, we will explore the concept of CRUD in cybersecurity, the types of CRUD, the associated risks, and how it is used to protect against unauthorized access, data manipulation, and data loss. We will delve into real-world examples of CRUD in cybersecurity, shedding light on the potential threats and vulnerabilities that organizations face. So, fasten your seatbelts as we embark on a journey to uncover the intricacies of CRUD in cybersecurity and its crucial role in safeguarding sensitive information from malicious intent.
What Is CRUD In Cybersecurity?
Crud, in the context of cybersecurity, refers to the four basic operations that can be performed on data: Create, Read, Update, and Delete.
These operations are fundamental to understanding and controlling how digital assets are managed and manipulated. For example, in data protection, it is crucial to monitor and control who can create, read, update, and delete specific information. By managing these operations effectively, organizations can mitigate the risk of unauthorized access and maintain the integrity and confidentiality of their data.
Understanding and implementing Crud operations are essential in safeguarding digital assets against potential cyber threats and ensuring comprehensive cybersecurity measures are in place.
What Are The Types Of Crud?
There are four types of CRUD operations in cybersecurity, each serving a distinct purpose in managing and securing data: Create, Read, Update, and Delete.
The ‘Create’ operation in CRUD involves the addition of new data or records into a system, and it plays a crucial role in managing information securely within the realm of cybersecurity.
It is essential for organizations to ensure that the ‘Create’ operation is performed with caution and adherence to data protection protocols. Without proper measures in place, there are inherent risks associated with creating and adding new data, such as the potential for unauthorized access, data breaches, or loss of sensitive information.
By implementing robust cybersecurity measures and access controls, organizations can mitigate these risks and safeguard their data against potential threats. For example, encryption and two-factor authentication can provide an added layer of protection to prevent unauthorized access during data creation and storage processes.
The ‘Read’ operation in CRUD involves accessing existing data or records from a system, serving as a fundamental activity for information retrieval and utilization within cybersecurity frameworks.
It plays a crucial role in allowing authorized users to obtain relevant data, but it also poses potential vulnerabilities for unauthorized access. Without appropriate security measures, sensitive information might be exposed, leading to severe consequences.
To control unauthorized access and potential intrusion, strong authentication processes, encryption techniques, and robust intrusion detection systems are imperative. These measures enhance the protection of data during the ‘Read’ operation, ensuring that only authorized individuals can access and retrieve the necessary information.
The ‘Update’ operation in Crud involves modifying or revising existing data or records within a system, presenting both opportunities and risks in the realm of cybersecurity.
It is crucial to recognize the potential for exploitation that comes with the ‘Update’ operation. Malicious actors could take advantage of vulnerabilities in security controls during the modification process, leading to unauthorized access, data breaches, or other cybersecurity threats.
Therefore, maintaining data integrity and security during updates is paramount to safeguarding sensitive information. Implementing robust security measures and regularly updating vulnerability patches are essential to mitigate the risks associated with the ‘Update’ operation.
The ‘Delete’ operation in CRUD involves the removal or elimination of existing data or records from a system, posing significant considerations for data protection and integrity within cybersecurity environments.
This operation has the potential to result in data loss and even contribute to data breaches if not implemented and managed with a robust cybersecurity framework. It becomes essential to integrate measures for incident response and recovery, ensuring that unauthorized or malicious deletions are thwarted.
By effectively implementing security measures, organizations can mitigate the risks associated with the ‘Delete’ operation, thus safeguarding the integrity and confidentiality of their data.
What Are The Risks Of Crud?
The CRUD operations in cybersecurity present various risks, including unauthorized access, data manipulation, and data loss, which can significantly impact the security and integrity of digital assets.
Unauthorized access poses a critical risk within Crud operations, potentially leading to data breaches, exploitation, and compromise of sensitive information in cybersecurity contexts.
Such unauthorized access increases the potential for cyber attacks, as malicious actors may exploit vulnerabilities to infiltrate the system, manipulate data, or disrupt operations. Authentication and authorization measures, such as multi-factor authentication and role-based access control, are crucial for preventing unauthorized entry. Intrusion detection systems play a key role in identifying and responding to unauthorized access attempts, bolstering the overall cybersecurity posture of organizations.
Data manipulation risks associated with Crud operations involve unauthorized alterations or tampering with data, potentially leading to misinformation, system exploitation, and compromised integrity within cybersecurity environments.
This type of manipulation poses significant threats to the confidentiality, availability, and authenticity of critical data. Cybersecurity professionals continually strive to anticipate and counter potential attack vectors, including SQL injection, cross-site scripting, and unauthorized access to database systems. Employing robust protective measures, such as implementing role-based access controls and comprehensive audit trails, is crucial.
Encryption plays a pivotal role in safeguarding data integrity by rendering it unintelligible to unauthorized persons, heightening resilience against manipulation attempts and ensuring the trustworthiness of data transactions.
The risk of data loss within Crud operations encompasses the potential for accidental or intentional deletion, corruption, or loss of critical information, necessitating robust data protection and incident response mechanisms within cybersecurity frameworks.
It is imperative for organizations to prioritize the implementation of comprehensive data protection measures to mitigate the detrimental impact of data loss incidents. Vulnerability assessments play a pivotal role in identifying and addressing potential weaknesses within a system or network, thus bolstering the overall cybersecurity posture.
By conducting regular vulnerability assessments, organizations can proactively detect and rectify vulnerabilities, reducing the likelihood of data loss events and fortifying their defenses against cyber threats.
How Is CRUD Used In Cybersecurity?
Crud operations are employed in cybersecurity through various protective measures, including safeguarding against unauthorized access, implementing data encryption, and deploying intrusion detection systems to mitigate potential threats and vulnerabilities.
Protecting Against Unauthorized Access
The protection against unauthorized access involves the implementation of robust authentication and authorization protocols, access controls, and user management strategies to mitigate the risk of unauthorized entry into digital systems within cybersecurity frameworks.
These protective measures are crucial in defending sensitive information and systems from potential cyber threats. Access controls, such as role-based access control (RBAC) and multi-factor authentication (MFA), play a significant role in preventing unauthorized access. Authentication mechanisms, including passwords, biometrics, and security tokens, provide an additional layer of security.
User authorization ensures that individuals only have access to the resources and information necessary for their roles, reducing the risk of unauthorized entry. Integrating these security measures is essential for maintaining the integrity and confidentiality of digital assets.
Implementing Data Encryption
The implementation of data encryption serves as a fundamental safeguard within CRUD operations, ensuring data confidentiality, integrity, and secure transmission within cybersecurity frameworks.
Encryption techniques, such as symmetric and asymmetric encryption, play a vital role in securing data at rest and in transit. Encryption algorithms, like AES and RSA, contribute to the robust protection of sensitive information. By utilizing encryption, organizations can uphold the principles of data protection, preventing unauthorized access and ensuring the security controls adhere to the highest standards.
Encryption is integral in mitigating the risk of data breaches, providing a crucial layer of defense against cyber threats.
Regularly Backing Up Data
Regular data backups are essential within CRUD operations, serving as a critical component of incident response, data recovery, and resilience planning within cybersecurity frameworks.
These backup strategies play a vital role in mitigating the impact of data loss due to cybersecurity incidents, ensuring that organizations can swiftly recover and resume normal operations with minimal disruption. By maintaining up-to-date backups, businesses can limit the potential damage caused by data breaches, ransomware attacks, or system failures.
Data recovery planning ensures that sensitive information is safeguarded, supporting data protection and privacy efforts in compliance with cybersecurity regulations and standards.
What Are Some Examples Of CRUD In Cybersecurity?
Several real-world examples illustrate the application of CRUD operations in cybersecurity, such as hackers gaining access to databases and deleting sensitive information, rogue employees manipulating user permissions, and malware attacks modifying or deleting crucial data within digital systems.
A Hacker Gaining Access To A Database And Deleting Sensitive Information
In this example, a hacker successfully gains unauthorized access to a database and perpetrates the deletion of sensitive information, underscoring the critical need for robust security measures and intrusion detection in cybersecurity environments.
The implications of such a data breach are far-reaching, as the compromised sensitive data could lead to financial loss, reputational damage, and severe legal repercussions. This incident highlights the potential vulnerabilities within the existing security infrastructure, necessitating a proactive approach towards strengthening defenses.
Effective intrusion detection systems play a pivotal role in identifying and mitigating such malicious activities, while incident response plans are crucial for minimizing the impact and initiating swift remediation measures in the event of a breach.
A Rogue Employee Changing User Permissions To Gain Unauthorized Access
In this scenario, a rogue employee manipulates user permissions to obtain unauthorized access, highlighting the insider threat and the importance of access controls, user management, and authentication in ensuring data security within cybersecurity frameworks.
This breach illustrates the potential risks of insider threats and the critical need for robust security policies and measures. Unauthorized access, if left unchecked, can compromise sensitive information and disrupt operations. Properly managing user permissions and regularly reviewing access controls are essential components of a comprehensive cybersecurity strategy.
In such cases, swift detection and response, combined with strict authorization protocols, play a pivotal role in mitigating potential damage. This example underscores the significance of continuously monitoring user access to prevent illicit activities and adhere to established security policies.
A Malware Attack That Modifies Or Deletes Data On A System
This example involves a malware attack that modifies or deletes crucial data on a system, showcasing the impact of malicious software and the imperative need for data encryption, vulnerability assessments, and incident response strategies within cybersecurity frameworks.
The consequences of such an attack can be far-reaching, leading to data loss, operational disruptions, financial damage, and the compromise of sensitive information. Incorporating robust data encryption methods and conducting regular vulnerability assessments are essential measures to fortify a system’s defenses against exploitation.
Incident response plays a pivotal role in swiftly identifying and containing breaches, minimizing the impact of a malware attack, and enabling the restoration of data integrity.
Frequently Asked Questions
What does CRUD mean in the context of cybersecurity?
CRUD stands for Create, Read, Update, and Delete. It refers to the basic functions necessary for managing data in a secure system.
Why is understanding CRUD important for cybersecurity professionals?
Understanding CRUD is crucial for cybersecurity professionals as it helps them design and implement security measures to protect sensitive data from unauthorized access or manipulation.
Can you provide an example of how CRUD is used in cybersecurity?
One example is access control, where users are granted specific levels of permission to create, read, update, or delete data based on their authorized role within the system.
How can a lack of proper CRUD implementation lead to cybersecurity threats?
If CRUD functions are not properly implemented, it can lead to vulnerabilities such as data breaches, unauthorized access, or data manipulation by malicious actors.
What are some best practices for implementing CRUD in a secure manner?
Some best practices include enforcing strong authentication measures, regularly updating and patching systems, and implementing data encryption for sensitive information.
Is there any difference between CRUD and RBAC in cybersecurity?
Yes, there is a difference between CRUD and Role-Based Access Control (RBAC). While CRUD refers to the basic data management functions, RBAC is a more complex system that allows for more granular control over data access based on specific roles and permissions. |
Flashcards in IS3340 CHAPTER 5 Deck (29):
Software that intercepts all incoming (and optionally outgoing) information, scanning each message or file for malware content is called ___?
Software designed to detect and mitigate spyware is called ___?
Software designed to detect and mitigate some types of malware, including mainly viruses, worms, and Trojan horses is called ___?
A condition in which a running program stores data that is larger than the memory location set aside for the data is called ___?
The extra data spills spills over into adjacent memory, causing other data and possibly instructions to be overwritten. An attacker can place specific data in this area to change the instructions a program executes.
The practice of identifying malware based on previous experience is called ___?
Software that is designed to infiltrate a target computer and make it do something the attacker has instructed it to do is called ___?
A common term used to describe malicious software, including viruses, worms, and Trojan horses, especially in combinations is called ___?
Software that modifies or replaces one or more existing programs, often part of the operating system, to hide the fact a computer has been compromised is called a ___?
The unique set of instructions that make up an instance of malware and distinguish it from other malware is called ___?
An organized collection of malware signatures used by antivirus or anti-spyware (or other anti-malware) software to identify malware is called ___?
Software that covertly monitors and records pieces of information such as Web surfing activities and all data process by the browser is called ___?
Software that masquerades as an apparently harmless program or data file but contains malware instructions is called ___?
A software program that attaches itself to, or copies itself into, another program for the purpose of causing the computer to follow instructions that were not intended by the original program developer is called ___?
Active malware that either exploits an unknown vulnerability or one for which no fix has yet been released is called ___?
1. Which type of malware is a standalone program that replicates and sends itself to other computers?
2. Which type of malware modifies or replaces parts of the operating system to hide the fact that the computer has been compromised?
3. Which type of malware disguises itself as a useful program?
4. Which term describes a unique set of instructions that identify malware code?
3. Rule set
5. Which of the following terms means identifying the malware based on past experience?
1. Heuristic analysis
2. Log file analysis
3. Signature analysis
4. Historical analysis
6. A signature database that is one month old may potentially expose that computer to how many new threats?
7. Which of the following terms describes a secure location to store identified malware?
3. Signature database
4. Secure Storage
8. Which of the following anti-malware components is also referred to as a real-time scanner?
3. Heuristic engine
4. Antivirus software
9. Which anti-malware tool is included with Windows 7?
1. Windows AntiVirus
2. Windows Doctor
3. Windows Defender
4. Windows Sweeper
10. Which of the following best describes a zero-day attack?
1. Malware that no longer is a threat
2. Malware that can exploit a vulnerability but has not yet been released
3. Malware that is actively exploiting vulnerabilities on computers that have not applied the latest patches
4. Malware that is actively exploiting an unknown vulnerability
Malware that is actively exploiting an unknown vulnerability
11. What is the best first step to take when malware is discovered soon after installing new software?
1. Uninstall the new software
2. Scan for malware
3. Update the new software
4. Install additional anti-malware software
Uninstall the new software
12. What is the best first step to take if initial actions to remove malware are not successful?
1. Install additional anti-malware software
2. Rescan for malware
3. Update the signature database
4. Disconnect the computer from the network
Disconnect the computer from the network
13. The Morris worm exploited this vulnerability:
14. Which type of malware covertly primarily collects pieces of information? |
This policy setting controls whether Office 2016 applications notify users when potentially unsafe features or content are detected, or whether such features or content are silently disabled without notification.
The Message Bar in Office 2016 applications is used to identify security issues, such as unsigned macros or potentially unsafe add-ins. When such issues are detected, the application disables the unsafe feature or content and displays the Message Bar at the top of the active window. The Message Bar informs the users about the nature of the security issue and, in some cases, provides the users with an option to enable the potentially unsafe feature or content, which could harm the user's computer.
If you enable this policy setting, Office 2016 applications do not display information in the Message Bar about potentially unsafe content that has been detected or has automatically been blocked.
If you disable this policy setting, Office 2016 applications display information in the Message Bar about content that has automatically been blocked.
If you do not configure this policy setting, if an Office 2016 application detects a security issue, the Message Bar is displayed. However, this configuration can be modified by users in the Trust Center. |
Weui Ransomware Description
The Weui Ransomware is a file-locker Trojan that's from the STOP Ransomware family. The Weui Ransomware can block files on Windows systems, digital media like documents, pictures, or audio, particularly, and withholds them while demanding a ransom. Users should have anti-malware products remove the Weui Ransomware immediately and recover from their last, secure backups as appropriate.
A Cyber-Soldier of Fortune Swoops in with Chinese Tags
The STOP Ransomware, a Ransomware-as-a-Service that roams the world with near-infinite variants like the Foqe Ransomware, the MOOL Ransomware, the Topi Ransomware or the Zwer Ransomware. Once again, it spills new threats out onto the Web, although the latest batch includes the novelty of a geo-regional clue. The Weui Ransomware, a somewhat China-inspired update, continues with the encryption and other integral traits of this family, sabotaging media files for Bitcoins.
Most of the expected effects of the Weui Ransomware infections of any relevance to victims focus on endangering data by encrypting media files through AES (and an RSA key, which it may either download or use according to an internal value). It also inserts another extension of 'weui' and wipes the user's Restore Point backups. Equally troublesomely, the Trojan can interfere with some security solutions and features and blocks some websites by changing the Hosts file's entries.
All of these attacks are for pressuring victims into a premium data recovery service through the STOP Ransomware family's traditional ransom notes. This text file asks for nearly one thousand USD in Bitcoins, with extras like a free demonstration and two e-mail addresses for support.
The extension is a string that different threat actors may set to various values. However, in the Weui Ransomware case, it seemingly refers to the user interface component of China's WeChat application. WeChat is a Tencent-developed program that includes social media, messaging, and mobile payment features, and one might describe it as China's 'super application.' Its global recognition and ties to China's government lead to the conclusion that the Weui Ransomware's threat actor targets WeChat users or, possibly, plans to make political statements during the attacks.
Breaking Up the Framework of Extortionist Plans
The Weui Ransomware's name being the same as WeChat's UI framework component, makes for a possible lead on its threat actor's nationality or just their planned victims. Still, all users of reasonably-modern versions of Windows are at risk from the encryption routine of the Weui Ransomware's family, which can stop files of almost all major media types from opening. Changing the name back to 'normal' doesn't reverse this attack; the extension is purely informative for the victim's benefit.
These issues are resolvable by users maintaining strong standards for Web-browsing security, such as installing updates, turning off unnecessary features and using strong passwords. A comprehensive backup also is crucial for recovering due to the strength of the STOP Ransomware family's encryption method. Standardized PC security products should isolate or remove the Weui Ransomware as it becomes necessary.
With random four-letter words for working with, the Weui Ransomware's name might turn out to be a coincidence. Whether it's targeting Chinese application users or not, it's a danger to those without the proper backups, no matter what language they're speaking.
Use SpyHunter to Detect and Remove PC Threats
If you are concerned that malware or PC threats similar to Weui Ransomware may have infected your computer, we recommend you start an in-depth system scan with SpyHunter. SpyHunter is an advanced malware protection and remediation application that offers subscribers a comprehensive method for protecting PCs from malware, in addition to providing one-on-one technical support service.
Why can't I open any program including SpyHunter? You may have a malware file running in memory that kills any programs that you try to launch on your PC. Tip: Download SpyHunter from a clean computer, copy it to a USB thumb drive, DVD or CD, then install it on the infected PC and run SpyHunter's malware scanner. |
The syntax to add an iptables rule is as shown below.
# iptables -I INPUT [line number] -s [ip address or subnet] -j ACCEPT
For example to add a new rule at line number 2 to allow subnet 192.168.0.0/24.
# iptables -I INPUT 2 -s 192.168.0.0/24 -j ACCEPT
Saving iptable rules
After configuring the iptables rules from the command line, it is required to save the iptable rules. It is important to save the list of iptable rules to make them persist across reboots or restart of iptable service.
# service iptables save |
This standard defines the responsibilities of the developers and of Web Services regarding the review of code prior to deployment to production servers.
It is the responsibility of all staff involved in the production of web applications to ensure the safety, integrity, and security of University resources. As such, ensuring that code is free of known vulnerabilities is essential. Developers should always complete a code review of their web applications prior to deploying them to production. It is recommended that vulnerability scans be performed by developers as part of the code review process.
All application content that is to be deployed to production should go through a code review. Code reviews are the sole responsibility of the application developers and will not be performed by Web Services on their behalf.
Automated vulnerability scanning is made available to developers by ITSP and can be an important part of the code review process. Whenever possible, vulnerability scans should be conducted against the QA tier. Web Services will assist developers with scheduling scans and results analysis upon request. When the scan identifies issues outside of developer control, Web Services will assist with vulnerability mitigation. No automated vulnerability scan is guaranteed to identify all issues and may flag issues that do not actually exist. For these reasons, a manual application code review is always needed. Certain situations, such as sites with no application code, may not warrant a vulnerability scan.
Vulnerability scans are required on a yearly basis for all applications that meet the following requirements:
- The application is known to process and/or access restricted data or has elevated access to other critical systems
- The application has not had a vulnerability scan in the last year for other reasons |
The HTTP Response Codes are used to quickly describe the success/failure of a HTTP Request. Trustev use the standard HTTP Response Codes as shown below:
- 200 OK – Successful request made.
- 400 Bad Request – The request is malformed.
- 401 Not Authorised – Authentication information is either incorrect or missing.
- 403 Forbidden – Authenticated user does not have access to the resource.
- 404 Not Found – When a non-existent resource is requested.
- 405 Method Not Allowed – When an HTTP method is being requested that isn’t allowed for that endpoint.
- 408 Timeout – The request has timed-out.
- 500 Unknown – Indicates an Internal Server Error.
We also include some extra information in the HTTP Response Message. We always recommend checking this should you run into issues during Integration, our response messages give a detailed description of the issue encountered, which should assist in debugging.
Should you find that the Response Codes OR Response Messages are not providing enough information, please contact our Integration Team, [email protected], and they will investigate the issue that you are seeing. |
|Computers, Materials & Continua |
A Novel Anonymous Authentication Scheme Based on Edge Computing in Internet of Vehicles
1Hunan University of Science and Technology, Xiangtan, 411201, China
2Hunan University, Changsha, 410006, China
3School of Information Technology, Deakin University, Geelong, 3220, Australia
*Corresponding Authors: Liang Bai. Email: [email protected]
Received: 30 August 2020; Accepted: 19 November 2020
Abstract: The vehicular cloud computing is an emerging technology that changes vehicle communication and underlying traffic management applications. However, cloud computing has disadvantages such as high delay, low privacy and high communication cost, which can not meet the needs of real-time interactive information of Internet of vehicles. Ensuring security and privacy in Internet of Vehicles is also regarded as one of its most important challenges. Therefore, in order to ensure the user information security and improve the real-time of vehicle information interaction, this paper proposes an anonymous authentication scheme based on edge computing. In this scheme, the concept of edge computing is introduced into the Internet of vehicles, which makes full use of the redundant computing power and storage capacity of idle edge equipment. The edge vehicle nodes are determined by simple algorithm of defining distance and resources, and the improved RSA encryption algorithm is used to encrypt the user information. The improved RSA algorithm encrypts the user information by reencrypting the encryption parameters . Compared with the traditional RSA algorithm, it can resist more attacks, so it is used to ensure the security of user information. It can not only protect the privacy of vehicles, but also avoid anonymous abuse. Simulation results show that the proposed scheme has lower computational complexity and communication overhead than the traditional anonymous scheme.
Keywords: Cloud computing; anonymous authentication; edge computing; anonymity abuse
In recent years, with the rapid development of scientific information technology, Internet of Things (IoT) technology has been widely used in various fields. The technical requirements for intelligent design in the IoT environment are growing . With the in-depth study of relevant researchers, the IoT technology has been continuously improved and gradually matured , it brought a great change to the Internet of Vehicles . In the Internet of Vehicles, the information communication and management handover between vehicles or between vehicles and roadside units keep growing, which leads to a large amount of traffic data transfer. Therefore, cloud computing is introduced into the Internet of Vehicles. However, in cloud computing, the cloud center is far away from the terminal vehicle, which is easy to form a large network delay . Meanwhile, because the open environment of Internet of Vehicles, it is more vulnerable to be attacked, so protecting the information security of vehicles focus on the top priority [6,7]. Technologies such as intrusion detection , data protection and identity authentication are used to protect the security of vehicle information. However, most of the traditional anonymous authentication schemes [10–12] have complex computation and large communication overhead, which makes them difficult to meet the actual situation of high-speed traffic in the vehicle communication network. However, with the development of edge computing in recent years, edge computing can well meet the mobility , low latency and trustworthiness of data . Therefore, in order to ensure user information security and real-time information interaction between vehicles in the Internet of Vehicles, this paper proposes a novel anonymous authentication scheme based on edge computing. In this scheme, the concept of edge computing is introduced into the Internet of Vehicles, and the distance and computing power are used as the reference for selecting edge nodes, and vehicle information is encrypted by the improved RSA encryption algorithm to ensure the purpose of information privacy and security. This scheme can greatly reduce the burden of roadside units (RSU), effectively utilize the computing performance of the edge terminal, and thus improve the certification efficiency of the whole system.
The rest of this article is organized as follows: Section 2 describes the work, Section 3 describes the network architecture and system objectives, and Section 4 is the proposed solution. The security analysis is shown in Section 5. Experimental and performance results are described in Section 6. Finally, the article is summarized in Section 7.
2 Related Work
In recent years, with the continuous development of technology, the Internet of Things (IoT) has become more and more common. It has produced a lot of data, and needs to have the privilege of virtual resource utilization and storage capacity, so that the integration of the Internet of Things and cloud computing has become more and more important [16–19].
Hussain et al. divided cloud-based Vehicular Ad-hoc Networks (VANET) into three main types: vehicle cloud (VC), cloud-based vehicle (VuC) and hybrid cloud (HC). VC uses vehicles to form a large cloud of services. It can be divided into two categories: static clouds and dynamic clouds. VuC allows ordinary nodes in VANET to connect to traditional clouds via RSUs. HC combines VC and VuC to get the best of both. Bhoi et al. proposed an RVCloud routing protocol for VANET to effectively send data to the target vehicle using cloud computing technology. In this protocol, the vehicle beacon information is sent to the cloud storage via a RSU. Since vehicles have fewer storage and computing facilities, information on all vehicles moving in the city is maintained by clouds. After receiving the data, the RSU sends a request to the cloud to obtain the best destination RSU information, which uses the smallest packet forwarding delay to send the data to the destination.
According to a report released by Forbes in 2015, cloud-based security spending is expected to increase by 42%. According to another study, IT security spending have increased to 79.1% by 2015, an increase of more than 10% per year. IDC showed in 2011 that 74.6% of corporate customers listed security as the main challenge. Therefore, protecting the safety of vehicle owner information in cloud computing is the top priority.
Zhang et al. proposed two new types of lightweight networks, which can achieve higher recognition accuracy in traffic sign recognition while retaining fewer trainable parameters in the model. Li et al. proposed a human pose estimation method based on knowledge transfer learning. In the estimation of human poses, first of all, by constructing a layered framework of “body–pose–attribute,” an attribute-based human pose representation model is constructed. The layered architecture makes it possible to effectively infer the characteristics of new human poses even when the training samples are small. In order to ensure the security of on-board cloud computing (VCC), a new security method was designed by using software defined network (SDN) technology , which uses pseudonyms, key management and list cancellation to protect vehicles from the attack of malicious nodes, and provides authentication, confidentiality, integrity and availability. Melaouene et al. proposed an intelligent RFID encryption and authentication scheme for filtering access applications in VANET environment. Huang et al. utilized a hierarchically defined network of software to optimize network management, thereby implementing a software-defined Internet of Things (SEANET) for energy harvesting. Specifically, it is such an architecture that can achieve flexible energy scheduling and stronger communication by separating the data plane, energy plane, and control plane. In the proposed scheme, the ECC authentication model is used to protect HF or UHF tags and reader authentication. Considering the trust relationship between mobile nodes in Vehicular Ad-hoc Network (VANET) was uncertain in the transportation network-physical system (t-cps), Sun et al. proposed a new t-cps VANET trust evaluation model based on member cloud. The proposed model addresses the trust uncertainty of fuzziness and randomness in the interaction between vehicles, and uses membership clouds to describe the uncertainty in the uniform format. In addition, a detailed description of the trustworthiness and an algorithm for computing cloud droplet and aggregate trust evaluation values is given. Nkenyereye et al. used pseudonym technology to create anonymous certificates to ensure the privacy of the vehicle required by the service. In fact, their anonymous credentials are based on ID signatures. The authentication and revocation of anonymous credentials are accomplished by batch validation and anonymous revocation list respectively.
There have also been some progresses in privacy protection. Wang et al. proposed an offline feature extraction model called LogEvent2vec, which takes log events as input to word2vec and extracts between log events and directly vectorized log events of relevance. The model reduces costs by avoiding multiple conversions, and the calculation time is 30 times shorter than word2vec. With the development of cloud computing and big data, the large amount of data collection makes the privacy of data more and more important. How to protect privacy has become an urgent problem. Wang et al. designed a deep learning-based data collection and pre-processing scheme, using semi-supervised learning algorithm for data amplification and label guessing. It can perform data filtering at the edge layer and clear large amounts of similar and irrelevant data. If the edge device cannot process some complex data independently, it will send the processed reliable data to the cloud for further processing, thereby maximizing the protection of user privacy. The scheme protects the privacy of users by filtering the data.
Li et al. proposed a VM packaging for page sharing, which takes into account constraints in multiple resources. The algorithm uses a heuristic algorithm that is better than the existing heuristics, which can reduce the VM required by up to 25% and reduce the memory page transfer by up to 40%. Yin et al. discussed a better scheme for data aggregation. First, it maximizes the gain by considering common data pruning capabilities and aggregation, and then selects a data set with higher pruning power and smaller size, and transmits the aggregated data on subsequent nodes. The overall idea is to construct AT by connecting a group of aggregation operations with the largest aggregation gain. Ma et al. proposed a caching placement strategy based on the cloud-based VANET architecture and the corresponding content retrieval process, which jointly considers the caching of vehicle layer and roadside unit layer. More specifically, the cache placement problem is modeled as an optimization problem that minimizes the average wait time while satisfying the QoE (Quality of Experience) requirements of the vehicle and is effectively solved by convex optimization and stimulus annealing (SA). Simulation results showed that the performance of this scheme is better than the existing caching schemes. Liu et al. proposed a novel cloud auxiliary messages down link transmission scheme (CMDS), through the scheme, the security message first with the aid of cloud computing in the cloud server is passed to the suitable relevant road nodes (gateway is both cellular and VANET interface bus), and then by vehicle to vehicle (V2V) communication between adjacent vehicles. Wang et al. proposed a safe and private-protected navigation scheme by using fog-based VANET’s vehicle space crowdsourcing. Fog nodes are used to generate and release crowdsourcing tasks and collaborate to find the best route based on real-time traffic information collected by the vehicle in its coverage area. At the same time, crowdsourcing vehicles can be reasonably rewarded. While entering its coverage area, the query vehicle can continuously obtain navigation results from each fog node and follow the best route to the next fog node until it reaches its desired destination. Their solution meets the security and privacy requirements of authentication, confidentiality and conditional privacy protection. Several encryption primitives, including the Elgamal encryption algorithm, AES, random anonymous credentials, and group signatures, are used to achieve this goal.
Since the public key infrastructure (PKI) and identity-based authentication protocols cannot avoid the inefficiency of authentication that need to check the certificate revocation list (CRL), a local identity-based anonymous message authentication protocol for Van trucks was proposed in LIAP . The certification authority is responsible for the long-term certification of each vehicle and roadside unit, and the RSU is responsible for the management and distribution of the master keys of local vehicles. These master keys can be used by vehicles to form pseudonyms to protect their privacy. In order to avoid inefficiency of authentication methods based on bilinear mapping and elliptic curve cryptography and to prevent illegal vehicle interference from attacking, HCPA-GKA proposed a group key protocol mechanism based on China Remainder Theorem (CRT) for distributing group keys to authenticated vehicles. When a vehicle joins and leaves the group, the group key can be updated. These group keys can be used to generate anonymous messages and to be authenticated. Pournaghi et al. proposed a relatively safer scheme called NECPA. NECPA stores the keys and the main parameters of the system in the tamper-proof device (TPD) of the roadside device. Because there is always a secure and fast communication link between TA and RSU, inserting TPD in RSUs is more effective than inserting TPD in vehicular OBUs. At the same time, the master secret key of TA is not stored in all OBUs in this scheme. Therefore, any attack to a single OBU will not threaten the whole network even if after attacked the whole vehicles need to be re-registered and change their secret keys.
3 Network Architecture and System Objectives
3.1 Network Architecture
The scheme model proposed in this paper mainly includes three entities: (1) Trust Authority (TA), (2) Roadside Unit (RSU), and (3) vehicle equipped with On-board Unit (OBU). Among them, the vehicle participating in message authentication and calculation is called Edge Computing Vehicle (ECV). As shown in Fig. 1, TA acts as a registry for RSUs and vehicles, which is trusted by all entities and responsible for distributing secret keys, storing user core information, etc. RSU acts as a gateway between the cloud center and the vehicle, and is also responsible for collecting information from the vehicle in its coverage area and passing it to the trusted agency TA through a secure channel (wired network); Vehicle OBU is divided into ordinary vehicle and ECV of edge computing vehicle. Ordinary vehicle acts as the data consumer, while edge computing vehicle and participating in the operation of data act as the data collector. Among them, Vehicle-to-Vehicle communication (V2V), Vehicle-to-RSU communication (V2R), Vehicle-to-Infrastructure communication (V2I) exist.
3.2 System Objectives
While ensuring the information security of users, the real-time information interaction between vehicles in the Internet of Vehicles is guaranteed. The objectives of this paper are: (1) Message authentication and integrity, (2) Identity privacy protection, (3) Traceability, (4) Resistance to replay attack, (5) Real-time.
4 Proposed Scheme
In this section, we introduce a novel method of anonymous authentication of vehicle network based on edge computation. (1) System initialization phase, (2) vehicle pseudonym generation and encryption phase, (3) edge vehicle election phase, (4) vehicle authentication phase, (5) edge vehicle information gathering phase, (6) tracking illegal vehicle phase when anonymous misuse occurs.
4.1 Initialization Phase
We assume that communication is secure during the initialization phase of the system. At this phase, TA generates the necessary system parameters and passes them securely to the RSU and tamper-proof device. Refer to RSA algorithm , and the specific calculation steps after improvement are as follows:
1. TA randomly selects two different large prime numbers p1 and q1;
2. Calculate n1 = p1q1 and compute the Euler function ;
3. Take an integer e1, which satisfies and is mutually prime between e1 and ;
4. Calculate ;
5. is the first layer public secret key, is the first layer private secret key, and ;
6. Select the second layer of two large prime numbers p2 and q2, and calculate the e2 and Euler function ;
7. Like 3) and 4), compute , and then use to encrypt n1, let .
And then, we have obtained that the public key and private key of the double RSA algorithm.
4.2 Pseudonym, Encryption and Decrypt Phase
At this phase, user registration is used to participate in the calculation. First, the user submits the registration application. TA first preliminarily determines whether the user is a legitimate user (If not in the database blacklist). If so, multiple encryptions will be carried out to protect the user information. If not, denial of service.
4.2.1 Generation of Pseudonym and Generation of Signature Information
The vehicle sends the RID of its real identity to TA for registration, TA checks whether the user exists. If it exists in the database, TA selects a random number ri (not public, stored in the tamper-proof agency), makes and , then calculates the pseudonym .
4.2.2 Encryption Process
When the user vehicle needs to send data, the information M , timestamp t and pseudonym VIDi are combined to form the information , Then encrypt to M′ and get ciphertext , and then encrypt to n1 and get .
4.2.3 Decryption Process
Decryption of by to , then decrypt c by to obtain . Decryption of by to , then decrypt c by to obtain .
4.3 Edge Vehicle Election Phase
The distance from the RSU and the computing resources contained in the vehicle unit determine whether the vehicle can be a marginal vehicle to participate in the calculation. Therefore, there are two measures.
4.3.1 The Distance
The distance from the vehicle to the RSU called . R represents the radius of the area covered by the RSU, and represents the distance between the vehicle and the RSU:
4.3.2 Compute Resources
Every vehicle has computing resource function , where represents the vehicle’s the amount of maximum computing resources and represents the amount of remaining resources.
Let the index be . When the attribute index of each vehicle is greater than or equal to 1, it means that the vehicle can participate in the calculation as the edge computing vehicle.
4.3.3 Vehicle Certification Stage
Any vehicle VAi sends a verification message to an unknown vehicle, that is, any number , pseudonym VIDi and timestamp t1, computing to get a ciphertext , to the unknown vehicle. Then the unknown vehicle needs to decrypt the verification message using its private key to get and t1. After that, the unknown vehicle needs to compute and , then send them back to VAi. Subsequently, VAi verify and timestamp t1. If successful, it determines that the user is a valid user.
4.3.4 Illegal Vehicle Tracking Phase
When vehicles use anonymous mechanisms to spread false traffic information or launch malicious wireless network attacks on nearby vehicles, we call it anonymous abuse of vehicles. When a malicious vehicle appears, the victim will send a tracking request to TA via the RSU. No matter where the encrypted message is, all of them contain the user’s pseudonym , The vehicle in the attacked area only needs to pass the information sent by the attacker to the RSU calculation to obtain the user’s real identity.
5 Security and Attack Analysis
5.1 Non-Forgeability of the Message Signature
The formation of a pseudonym consists of: and . Because the public key is known, if a malicious vehicle wants to forge a signature, the attacker needs to obtain his own private key to form a pseudonym. The private key is kept by tamper-proof authorities and cannot be easily obtained by an attacker. The attacker can only obtain its public key, but to forcefully crack the private key from the public key, the discrete logarithm problem needs to be solved. Moreover, this scheme adopts the double RSA encryption, which is more difficult to crack than the general algorithm, so the attacker can’t get a feasible solution in polynomial time. Therefore, an attacker cannot forge the signature information of a legitimate vehicle.
5.2 The Anonymity of the Scheme
Vehicles use pseudonyms when they interact with other vehicles in the Internet of Vehicle. According to the discrete logarithm problem, although other vehicles know VIDi and n2, there is no way to calculate ri that the user stored in the tamper-proof facility, and even the vehicle itself cannot disclose it. Therefore, for users other than the owner of the pseudonym, the real identity corresponding to the pseudonym cannot be obtained using the pseudonym name, public key, etc.
5.3 Resistance to Replay Attack
In the process of information transmission, we add the concept of timestamp t . In order to ensure timeliness, the information receiver should check whether the information exceeds the deadline in the first time. Assume t′ is the message receiving time t′ and represents the estimated network time delay and transmission time. If is satisfied, the information is valid, otherwise the information is invalid and the service is denied.
The pseudonym generation of the vehicle is the ri generated by the tamper-proof mechanism, and a new pseudonym needs to be generated after every information transmission interaction. Therefore, it is impossible for the attacker to track the source according to the pseudonym, which ensures system unlinkability.
5.5 Traceability of the Scheme
If a TA wants to track the real identity of the vehicle, it first finds the vehicle’s pseudonym VIDi, and then calculates:
Then the TA can get user’s real identity. So in the actual tracking process, other vehicles only need to provide the pseudonym used by attacker and a TA can find the malicious user’s real identity without the participation of all vehicles. In addition, even if the malicious user has a new pseudonym, the TA can also find out its real identity through the previous pseudonym.
6 Performance Analyses and Simulation
6.1 Computational Cost Analysis
Since Internet of Vehicles is a delay sensitive network, we take time cost as one of the comparative measures. For the sake of comparison, to facilitate subsequent comparison, Tmu denotes the time cost of modular multiplication, Tp denotes the time cost of pairing computation, and Th denotes the time cost of performing a hash operation.
Tab. 1 presents the computational overhead between LIAP , HCPA-GKA , NECPPA and our scheme. We can easily find that because of the use of edge computing, our scheme greatly improves the utilization rate of resources and reduces the computing overhead.
6.2 Performance Analysis
Tab. 2 presents the property comparison between LIAP , HCPA-GKA , NECPPA and our scheme. From Tab. 2, we can see that LIAP and HCPA-GKA scheme lack the ability of threshold tracking. NECPPA is time-consuming because it still uses the conventional cryptosystem for anonymous authentication. Generally speaking, our scheme is relatively balanced and comprehensive.
This paper uses the open source Veins simulation framework to simulate the scheme. Veins is an open-source simulation system for vehicular communication network environments, which consists of event-based network simulators and road traffic simulation modules and also includes basic 802.11p/1609.4 modules and a simple application layer data generation framework. It uses OMNeT++ software as a network simulator and open source traffic simulation software SUMO as the generator for road traffic simulation scenarios. SUMO integrates important aspects such as vehicle trajectory, driving rules, driving habits, etc., and communicates with external programs such as Veins, OMNeT++ , NS2 through the Traci expansion package.
In order to form a contrast, we stipulate that the speed of vehicles is 20 m/s. With the increase of average vehicle density, the network delay and packet loss rate of the four schemes are observed.
From Figs. 2 and 3, we can easily see that edge computing can greatly improve the response speed of the Internet of Vehicles, reduce the delay and packet loss rate, and ensure the information security of users under the double RSA algorithm in our scheme.
The notion of cloud architecture is extensively applied in Internet of Vehicles. But for rush-hour traffic, cloud architecture has some disadvantages. In this paper, the edge computing, combined with the improved RSA algorithm, can solve the problem. First of all, the dual RSA algorithm greatly improves the information security of users, which is difficult to be cracked within the time limit. At the same time, the use of timestamp t prevents replay attack when information interaction. Secondly, edge computing design makes full use of the idle resources of surrounding vehicles by utilizing edge computing vehicle to serve other vehicles, which greatly improves the timeliness and reduces the transmission delay. Therefore, in general, the proposed scheme ensures low latency, low packet loss rate and high security. It is consistent with the use of Internet of Vehicles environment.
Funding Statement: The financial support provided from the Cooperative Education Fund of China Ministry of Education (201702113002, 201801193119), Hunan Natural Science Foundation (2018JJ2138) and Degree and Graduate Education Reform Project of Hunan Province (JG2018B096) are greatly appreciated by the authors.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.
|This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.| |
Containers are transforming how enterprises deploy and use applications. In traditional virtualization, the server runs a hypervisor, and then virtual machines with entire guest operating systems and software run on top of the hypervisor. They allow more versatility than traditional physical servers since they simplify management and provide faster provisioning of applications and resources, but they still have significant limitations. The files are large, often on the scale of multiple gigabytes. They’re not very portable.
This leads me to two of the biggest draws of containers: increased efficiency and portability. With containers, you can run software without worrying about operating systems or dependencies. An operating system runs underneath the containerization platform. Then, instead of having to build a production environment with the right settings and libraries, the container already has that build in. Because containers do not depend on the underlying OS, they are more portable than traditional virtual machines. You do not have to package the container with an entire OS: the files you need to run an instance add up to mere megabytes, not gigabytes.
Common Container Security Risks
However, as with any exciting new technology, there are inevitably security risks that come along with it. That doesn’t mean avoiding containers. Instead, by keeping the risks and container security best practices in mind, you can protect your business while gaining the benefits. As you plan how to adopt and expand containers in your environment, here’s what to keep in mind.
As with isolation between instances in traditional virtualization, the isolation between containers helps make them attractive from a security standpoint. However, isolation capabilities do not make containers safe by default: there is a level of risk. After all, just like your security team, attackers also know that finding a container escape flaw in the platform can grant access to sensitive data in other containers.
Also remember to consider isolation from a network perspective. Despite the fact that modern containerization platforms offer network segmentation, real-world implementations of container platforms often do not take advantage of those network segmentation features. Security teams may assume the containers are secure enough by default. But without network segmentation, it becomes a lot easier for an attacker to traverse from one compromised container to other vulnerable ones on the same network.
Containers are attractive because they are so portable and so easy to set up. We are seeing that attackers are leveraging these features of containers to get into environments. Attackers will create their own malware-laden containers and upload them to public repositories such as Docker Hub. Before running containers, your team will need to consider the source and assess the security of a container to make sure you are running trustworthy software and not giving data thieves or crypto miners an invitation to your network.
Insecure Configuration of Other Components
Apart from the security of individual containers, you must also consider the other components in the environment. You must update and securely configure the host OS, harden the containerization layers and any orchestration software, and configure accounts based on the principles of least privilege. After all, not only can machines running containers be vulnerable to OS-level attacks, but real-world attackers are already focusing on insecurely configured containerization layers.
Financially motivated attackers, in particular, have seen the opportunities. Here are a couple recent examples. Last year a cryptocurrency mining group looked for Docker instances that allowed Docker commands to be run without a password. The attackers then ran commands to make Docker set up an Ubuntu Linux container that they could then use to turn off security features, stop any competing cryptomining ransomware, and run Kinsing. Kinsing is a strain of cryptomining malware that also has the ability to steal credentials and keys, look through previously run commands, and identify other vulnerable machines on the network. Earlier in 2021, another cryptocurrency-mining group was identified that was looking for open Docker management ports and stealing API keys in order to put malware in Docker containers. Both of these examples underline the importance of proper configuration.
Just as with traditional computing and OS-level virtualization, securing and protecting secrets remains a prime security concern when using containers. In the context of containers, you need to think about protecting sensitive information such as credentials, API keys, and tokens at every level: the containerization platform, orchestration platforms like Kubernetes, and the content of individual containers.
Secret management flaws can emerge in many ways. Developers may hard-code credentials in scripts placed in containers. Secrets may be saved in an insecurely configured key management system. They not be rotated regularly. Any of these kinds of flaws could lead to an attacker gaining access to things they shouldn’t, or being able to cause a more extensive or expensive consequence than they could have otherwise.
Securing Your Container Environment
So, how can you stay secure while taking advantage of the portability and flexibility of containers? I recommend the following steps as a starting point.
Hardening a Container Environment
The first step is to assess what containers your business is using. Ensure that your environment is only using trusted containers from known sources. Next, accurately document all containers in the environment. This can be a challenge, due to how easy containers are to set up and how portable they are. But like any physical assets, knowing your attack surface is a fundamental step toward keeping the network secure. Furthermore, ensure that containers are on properly segmented networks. Even if you are taking every precaution with container security, segmenting containers at the network layer can help mitigate the effects of a compromise in a single container.
Configuration and patching also matter, both in containers and the systems underlying them. Host operating systems should be configured securely and patched regularly, as should the containerization layer. Containers should also be part of the patching schedule. Containers are immutable objects, so software updates take the form of replacing the previous version with an updated one. Those updates are equally important to traditional software updates.
Both the foundation layers that the container sits on as well as the container itself require ongoing monitoring and regular security testing. Even the most knowledgeable and security-conscious businesses can benefit from penetration testing. Even in environments where knowledgeable security teams have taken pains to secure container infrastructure at every level, a small misconfiguration in security policy can lead to a complete compromise of a container environment. With so much at stake, I can’t emphasize enough the prioritization of regular penetration testing.
Additional Resources for Container Security Best Practices
Though containers are a relatively new technology, enough enterprises have adopted them that trusted sources publish hardening guidance for container infrastructure. This includes CIS, which publishes security benchmarks for both Docker and Kubernetes, as well as OWASP, which has released a Docker security cheat sheet. You can use these to help develop your baseline for checking or planning container environments.
Your Partner in Container Security
For many organizations, containers and container security are something of a new frontier. There is some guidance out there on best practices, but there are still many unknowns, and, rightfully so, questions about the secure deployment of containers. It’s an important topic, and one the team at Kroll, including myself, have been digging into and researching for years now with the goal of helping enterprises make the most of the technology.
Our team has a deep bench of penetration testers who not only have broad-based penetration testing experience but are experienced with both using and testing container technologies like Docker and Kubernetes. If you’re interested in talking to a consultant about how to strengthen your container security, our team would be glad to help. Contact our team here. |
How to find out why application eats that much memory?
• Run profiling-enabled application.
• Connect to application. DO NOT record allocations as they are not needed to solve this task.
• Capture memory snapshot. To identify the moment when to capture the snapshot, use Telemetry to see when and how the used memory grows. Also, a snapshot can be captured automatically on low memory and/or on out of memory.
• Open the snapshot and use the Statistics section in the “All Objects” view.
Memory leak is an existence of objects that are not needed anymore according to the application logic, but still retain memory and cannot be collected because they are referenced from other live objects, due to a bug in application itself.
You can suspect the presence of memory leaks with the help of Telemetry, by watching how used memory grows in the “Memory” tab. You can use “Force Garbage Collection” to see whether some of the objects that consume the memory can be collected, thus decreasing used memory. If after those explicit garbage collections the used memory remains on the same level or decreases insignificantly, this possibly means you have a leak. (“Possibly,” because it can turn out not to be a leak, but simply high memory consumption)
Also consider the “capture snapshot on low memory” feature in YJP tool. Snapshots captured automatically using this feature may possibly be captured in the state when memory usage reaches the specified threshold because of memory leaks. Thus, the snapshots will provide information sufficient to discover and fix the leaks. |
The full routing table, including full BGP, may contain fewer than 700K records in 2020. Downloading and processing such a large amount of data is time-consuming and may not provide any relevant information about the internal IP addressing scheme.
In cases where we expect to discover a router with a full BGP table, we can limit the total number of BGP routes stored in the database.
You can find the threshold configuration in the Settings → Advanced → Discovery tab.
The lower limit available is currently 10000 BGP routes. The IP Fabric will read the full routing table but will filter BGP routes per the threshold before storing them in the database. |
TL;DR A common pitfall is to think that the content of a running process and its corresponding executable file (i.e., the content of the file stored on the disk) are identical. However, this is not true, as activities such as memory paging or relocation can affect to the binary code and data of a program file when mapped into memory. We have developed two methods to pre-process a memory dump to facilitate, by undoing the relocation process, the comparison of running processes and modules. We have implemented these methods as a Volatility plugin, called Similarity Unrelocated Module (SUM), which has been released under GNU Affero GPL version 3 license. You can visit the tool repository here.
In a previous post we have already motivated this research, as well as giving some background on the Windows memory subsystem and the Windows PE format. Furthermore, we also explained the Guided De-relocation process, which is a method to identify and undo the effect of relocation relying on specific structures of the Windows PE format. In particular, the Guided De-relocation process uses File Objects, a kernel-level structure that represents the files mapped into the kernel memory. You can read more on this method in our previous blog post or in our recent publication in Computers & Security. In this post, we are going to introduce the other method, named Linear Sweep De-relocation.
Linear Sweep De-relocation Method
Unlike Guided De-relocation, this method works independently from the File Object structures. Therefore, it can be applied to any module in a memory dump, regardless there is a File Object that represents such a module.
The algorithm of this pre-processing method is giving below. In summary, the algorithm works in all bytes of a module in two phases. The first phase is devoted to analyze structured data, i.e., all the bytes of the PE header and the data directories are processed, looking for memory addresses to de-relocate them, if needed. At the same time, these bytes are tagged as visited. Recall that this de-relocation process consists in leaving the two-less significant bytes unmodified, while zeroing the others (two bytes for 32-bit processes, and six bytes for 64-bit processes).
The second phase is devoted to unstructured information. First, the bytes of the lookup tables are tagged as visited. Byte patterns and strings are tagged next. Finally, the rest of bytes that have not been visited are processed. For every non-visited byte, we build sequences (in bytes) of valid assembly instructions. In this regard, we get slices of the contiguous 15 bytes starting at the address of b, considering the maximum length of Intel assembly instruction. Our algorithm processes these sequences in an optimized way to avoid redundant disassembling. In particular, we iterate in each instruction of the sequence, marking the beginning of every instruction in an auxiliary structure until we detect an instruction which was previously marked as the beginning of an instruction in another sequence. In such a case, we discard the current sequence of instructions since we have reached a subsequent sequence of instructions already recognized by another previous sequence of instructions and thus, the previous sequence will always be greater in length than the current one. We rely on the Capstone disassembly framework to obtain the valid sequences of instructions.
Finally, we select the longest byte sequence of valid assembly instructions and iterate in each instruction in this sequence, tagging every byte of the instruction as a visited
byte and checking if it contains an instruction that has an operand which is a memory address. If so, the de-relocation process takes place.
Let us illustrate how the processing of sequences of instructions works by providing an example. Assume the following snippet of real assembly code of a Windows library (instructions are shown in hexadecimal representation and in mnemonics) whose bytes were not identified by any of the previous steps of the algorithm:
Consider now a slice of 15 bytes. We start disassembling at the byte
0xFC, getting the code snippet:
This sequence is only 1-byte long, as the next byte is not a valid instruction. We then update the auxiliary structure indicating that the sequence starting at the byte
0xFC is 1-byte long and ends in the byte
0xFE (value -1):
The sequence starting at the next bytes are also invalid, till the byte
0xE8. Starting at this byte, we obtain the following code snippet:
This sequence is 17 bytes long, so the corresponding position is updated in the auxiliary structure appropriately. Note that the starting bytes of other instructions are marked with a value -1:
The code snippet starting at the byte
0x39 has an instruction which was already part of a previous snippet, as indicated by the value -1 found on the structure:
Therefore, the processing of this code snippet is skipped and discarded. The auxiliary structure is updated appropriately:
This process iteratively repeats until the end of the sequence. The auxiliary structure is finally as:
In this case, the longest sequence found was the one starting at the byte
0xE8. The bytes in the slice are marked as visited, and if any instruction has a memory operand targeting the virtual memory range of the process, its address is de-relocated. Now, the next slice would start at the byte
0xFE (in address
0x1016), which is the end of the longest sequence found.
A brief explanation of our experimental scenarios is given in our previous post. For the sake of brevity, we refer the reader to the previous post or to the paper for more experimental evaluation and further discussion.
The figure above shows the aggregated similarity scores of the Raw scenario as violin plots, which show the median as an inner mark, a thick vertical bar that represents the interquartile range, and the lower/upper adjacent values to the first quartile and third quartile (the thin vertical lines stretching from the thick bar). As shown, the similarity scores in 32-bit are more disperse and the lower/upper adjacent values are normally all in the range of possible scores, independently of the module or the algorithm.
The results in 64-bit architecture are more stable than in 32-bit architecture. Note that the median of the similarity score is near to 100 for all algorithms and all modules. Only the lower adjacent values of
spoolsv.exe have a wider interval. We have manually checked these results and found that they are due to the modules retrieved from Windows 8. In particular, the dissimilar bytes are caused by lookup tables within the code section of the modules.
These good results for 64-bit architecture may be due to the new addressing form introduced with the 64-bit mode in Intel, RIP-relative addressing, which guarantees that no assembly instruction incorporates an absolute memory address within the binary representation of the instruction itself.
When the Linear Sweep De-Relocation pre-processing method is applied, the results vary significantly. As shown below, the results of the similarity scores are extremely good. Note that this method is even better than the Guided De-relocation pre-processing method, as it can be applied to all modules within a memory dump, and not only the ones with their corresponding File Objects being placed in memory. Regarding the 64-bit scenario, the results in the case have almost perfect similarity, having some outsider values in the case of the
sdhash algorithm. As before, these almost perfect results may be motivated due to RIP-relative addressing of Intel x64.
As we have explained in our previous post, both pre-processing methods are implemented in a plugin for the Volatility memory analysis framework. Our plugin, called Similarity Unrelocated Module (SUM), is an improvement of our previous tool ProcessFuzzyHash. SUM have been released under GNU AGPL version 3 license and is available in GitHub.
And that’s all, folks. This post closes our series on the two de-relocation processes that we have presented in our recent paper in COSE. Feel free to use our plugin and send us your impressions and even ideas for improvement. We will be delighted to keep improving our tool and keep researching on this area of memory forensics! |
Network of calls placed (3A) and received (3B) per capita across the urban system. Figure 3A shows the number of calls placed per capita from origin to destination cities in Côte d’Ivoire. Calling patterns show heavy flows into Abijdan, and a northward trend in calls placed in more southern places. Figure 3B shows calls received per capita. The northern part of the country displays relatively fewer calls into or out of its cities. |
In the world of audio networking the current buzzword is AES67. This is the interoperability standard that allows different audio networking protocols to pass audio to each other.
In the past there were different audio networking protocols but they were completely incompatible. Then it was realised that a number of these different protocols were in fact very similar. If they made small changes then they could communicate with one another.
In order for two networked devices to send audio to each other they need to know each other’s address and also how they should manage the connection between each other so they can communicate together.
If I need to make an international phone call I need to know two things – first the telephone number to call and then we need to agree what language we are going to speak in. The telephone number is the address and the language is the connection method.
AES67 uses a system called SIP for managing unicast connections. SIP stands for Session Initiation Protocol. It sets up a connection between two end points such as a mixer to an amplifier. The two devices signal to each other that they can accept connections of a particular type and they agree how they will communicate with each other. This process is essentially automatic and is unseen by the user.
However before SIP can do its job, the two devices have to know each other’s address, because SIP only deals with the method they will use to communicate.
You can of course just type in the addresses of the two devices just like getting a telephone number and dialling manually. Another way to know the addresses of devices on the network would be to have a SIP server. This might be a PC sitting on the network. The server would contain details of all the connections on the network, a bit like having a phone book. When two devices want to start communicating they would ask the server for the address of the other.
As an alternative to a SIP server, you could have a discovery mechanism that would find and identify end points on the network. Discovery is the ability for equipment to automatically find other items. In an ideal world you would switch on your PC or mixing console and it would list all the other items by a friendly name, and say how they can communicate.
However our world is not ideal and discovery isn’t as elegant as that. The IT industry has invented different discovery methods and they each have different features and disadvantages.
Two common discovery mechanisms are Bonjour or SAP (don’t confuse it with SIP) and these could be used to show what is on the network. These methods automatically announce what is on the network rather than have a known list of connections like on a SIP server.
Bonjour was invented by Apple but is freeware for anyone to use. It was created to advertise/discover devices such as printers on a local network. It works well for simple networks but wasn’t really designed for large ones.
SAP (session announcement protocol) broadcasts information to all devices on a well-known multicast address. All devices listening to that port receive periodic information on available sessions. Essentially devices receive an updated business card from any participating device from time to time.
However things get a little more complicated. Bonjour is a very flexible and powerful mechanism for advertisement of different kind of services, while SAP is strictly related and limited to announcing available multicast connections sessions only.
Using Bonjour, you can announce the SIP uri for a unicast connection as well as relevant connection parameters for multicast sessions – plus lots of other useful services. But as mentioned above it doesn’t work well on large networks, especially not on corpoprate or routed networks.
AES67 is designed to be a lowest-common denominator to allow audio to pass. Normally this would be used to exchange audio signals between two completely different audio systems, such as between a
broadcast truck and an installed sound installation. Its creators deliberately left out superfluous features and their intention was to make things as flexible as possible. Had they
implemented a specific method of discovery, that might have prevented some protocols from becoming AES67 compatible.
Advertisement and discovery as well as directory services are used for lots of other services purposes on a network. In larger networks, these services are already in place and can be used for AES67 advertisement streams as well. If AES67 had mandated for a specific protocol, that protocol would have to be on place in parallel to any already existing service. This certainly does not impose a problem to a small self-contained live or install sound setup, but the IT guys managing the whole network wouldn’t like it.
The key to the future success of AES67 is to be as compatible as possible. If you want specific features, then you should specify equipment that uses your favourite protocol.
Whilst automatic detection of equipment is helpful, it’s not necessarily the most important thing when constructing an audio system. Its very common for engineers to note down the IP addresses of their gear where they don’t want to have dynamic addressing. We all do detailed planning when designing systems, don’t we?
Sometimes we are required to use a particular IP addressing scheme so it fits into the larger IT plan, which again means we have to keep a log of IP addresses.
So whilst automatic discovery is helpful, it isn’t the most important feature when connecting audio networks together. |
Cuckoo is an automated dynamic malware analysis platform which allows for the analysis of submitted artefacts within a range of custom configured guest operating systems.
Analysis environments may be created for Windows, Linux, MacOS and Android, with all manner of filetypes able to be analyzed through the Cuckoo platform. Including, executables, office documents, pdf files, emails, and even hands-on execution of malware with network connections able to be routed through Tor.
The Cuckoo platform allows for the capture of memory (using Volatility), and even captures API executions through the guest virtual machine. The resultant capture is then analyzed through Cuckoo’s utilities, with summarized reports generated in addition to detail reporting.
Cuckoo operates as a virtual machine within the Cuckoo host operating system. The host operates on a Linux OS, with the analysis VMs being segregated from the host using VirtualBox and network profiles assigned which control how network connectivity is presented to the analysis VM.
Ordinarily, the analysis is presented a virtual network interface (vboxnet0) with routes added to provide connectivity between the vboxnet0 subnet and the external interface to the Internet. However this can also be adapted to only allow connection through a Tor circuit (further obfuscating the source of network traffic and potentially not disclosing the operation of the Cuckoo platform).
There are however other things which can disclose the operation of a sandbox, and these are referred to as traces by the developers of Cuckoo. These traces should be reduced so far a possible to reduce the likelihood of a piece of sandbox-evading malware from detecting the environment and preventing analysis of the sample.
Some malware is also able to detect the absence of normal usage within the Analysis VM, which may also give away the presence of an analysis environment. There are strategies in configuring your environment which can assist in reducing the likelihood of this causing a detection by the malware.
What can Cuckoo achieve?
Dynamic malware analysis is the analyses of artefacts for malicious content by executing said artefact within a controlled environment. Telemetry is captured within that controlled environment for processing and analysis and then conversion into an intelligence report.
Functions performed by Cuckoo include capturing of trace calls performed by processes, file captures and activity, memory dumps, and network captures.
Traces of calls performed by all processes spawned by a sample
Traces generated by a sample are captured within Cuckoo, with behavioral analysis applied post-execution to determine if the sample is performing an action which could be considered suspicious or malicious.
Files being created, deleted, modified, and downloaded by a sample
Files which the sample touches within the Analysis VM are captured and extracted to the Cuckoo host and further analyzed by a selection of utilities (including Yara). These results are incorporated into the detailed report generated at the end of the Cuckoo analysis task.
Memory dumps of a sample
Memory generated by processes are captured within the analysis VM and then presented to the Cuckoo analysis platform. Volatility is then run over these memory dumps to find interesting items within the sample for incorporation into the detailed report.
Network traffic captured in PCAP format
Network traffic generated within the Analysis VM is captured with TCP Dump and then presented to Cuckoo for further analysis, and lookups to configured external services (e.g. VirusTotal and MISP) for threat detection.
Screenshots of the Analysis VM during analysis
Screenshots from within the Analysis VM are captured wherever significant activity occurs within the Analysis VM, and are timestamped within the technical analysis. This creates a type of timeline within the execution time of the analysis and records what visual changes occur through an analysis.
Complete memory dumps of the Analysis VM
Lastly, a complete memory dump is captured by the Cuckoo platform of the Analysis VM for complete analysis and reporting. This memory dump can take some time to capture and analyze, so system resources for this analysis need to be considered. |
Interventions in the field of security are complex, not only because insecurity problems are often unstructured – there are no ready-made solutions available – but also because many different actors and policy levels are involved. In this sense, the problem of coordination is present in every aspect of security policy. The local security policy can be seen as a place where different visions of (in)security meet.
The starting point of the field of study “Local Safety Policy” is that the unclear concept of “security” is given concrete form at the local level. It is the ambition of this working group to stimulate the discussion about the way in which the concept of “local security policy” will be implemented. To this end, attention will be paid to three topics:.
These are: policy coordination, a broad interpretation of the concept of “security” and knowledge management. In addition, the working group wants to focus on the different aspects of the local security policy. The different phases of the security chain (proaction, prevention, repression and aftercare) will be used as a conceptual framework. The ambition of the working group is to help translate loose information into concrete knowledge. Not only on the basis of scientific research, but also on the basis of practical insights.
With the workingroup “Local Integral Security Policy”, the CPS wishes to reach actors from other policy areas as well as traditional security professionals. Civil society as well as the private sector will be involved.
0032 9 223 24 11 / 0032 (0)476 20 29 40
Office and postal address: |
Get/Set HTTP Headers in Go Request
HTTP headers, we all need ’em 😉
Here’s how you can get and set these headers within your Go requests. These include request coming into your router handlers and requests you are sending out to other systems through
net/http. This can be thought of as being the same as reading headers from a request and creating new ones.
First we’ll start with reading them from the request.
The headers are accessible through the
Header part of the request - and from here you can get a specific header, through calling
If the header isn’t set, or is empty, it will return an empty string.
You can call
.Set() in a similar way to the reading the headers, but you will also need to pass in the value to set it to. In our example above we set the content type of the request to json. |
Detecting the Undetectable: Man + Machine
Rapid Detection Service helps prepare your organization for advanced cyber attacks, before and after they happen. Our fully managed service is designed to detect the most skilled of attackers, whether they're using malware or non-malware tactics, techniques, and procedures. It enables you to respond to threats promptly, with actionable guidance from our experts.
Our service is committed to the following:
Cyber security experts keeping watch
over your environment 24x7x365
Max 30 minutes from breach detection to response committed in Service Level Agreement
Immediate return on investment
as a turnkey managed service
How does Rapid Detection Service
detect and respond to human-conducted attacks?
How Does a Targeted Cyber Attack Usually Happen?
Attackers will first gain access to your IT infrastructure. This typically happens either by exploiting a known vulnerability in one of your servers, or by using a combination of spear-phishing emails and a web or document exploit targeting, for example, one of your customer-facing teams.
After gaining the initial foothold in your IT infrastructure, the attackers will try to access the data or gain the control they are after.
Typically, they accomplish this by using existing IT administrator tools included in Windows, Mac and Linux operating systems such as PowerShell, Windows Remote Management and Service Commands.
How Do We Detect?
Rapid Detection Service includes lightweight intrusion detection endpoint and network decoy sensors that are deployed across your IT infrastructure. The sensors monitor activities initiated by the attackers and will stream all information in real-time to our cloud.
Our cloud hunts for anomalies in the data by using a combination of advanced analytics such as real-time behavioral analytics, big data analytics and reputational analytics. Anomalies are hunted from two perspectives: known and unknown bad behavior.
The use of different types of analytics means that attackers are unable to successfully use evasion tactics designed against a specific analytics type.
How Do We Respond?
Anomalies are flagged to our analysts in the Rapid Detection Center, who work 24x7x365 to verify them and filter out false positives.
Once our analysts have confirmed that an anomaly is an actual threat, they will alert you in less than 30 minutes. Our analysts will guide you through the necessary steps to contain and remediate the threat. We also provide detailed information about the attack, which can be used as evidence in criminal cases.
Our on-site incident response service is also available to serve you in difficult cases or if your own experts are unavailable.
F-Secure's security experts have participated in more European cyber crime scene investigations than any other company. While our experts are tracking the pulse of cyber threats, you stay up to date with the latest threat intelligence.
Data Events/ Month
Collected by ~1300 end-point sensors
After RDS engine analysis of the raw data
RDC threat analysts confirmed anomalies and contacted the customer
Confirmed by the customer
Finding a needle in a haystack – a real world example
In a 1300-node customer installation, our sensors collected around 2 billion events over a period of one month. Raw data analysis in our backend systems filtered that number down to 900,000 events.
Our detection mechanisms and data analytics then narrowed that number to 25. Finally, those 25 anomalies were analyzed and handled by experts in our Rapid Detection Center, and 15 were confirmed by the customer to be actual threats.
In each of these 25 cases, our Rapid Detection Center alerted the client within 30 minutes from the moment the anomalies were flagged as actual threats.
Our team is at your service 24x7x365
At the core of Rapid Detection Service is our Rapid Detection Center, which is the base of operations for all of our detection and response services.
At the center, cyber security experts work on a 24/7 basis, where they hunt for threats, monitor data and alerts from customer environments, flag anomalies and signs of a breach, and then work with our customers to respond to real incidents as they take place.
Staff at our Rapid Detection Center are trained to handle a variety of tasks
Their main tasks fall into three different roles:
First responders who monitor the service, hunt for threats and maintain contact with the clients
Tackle complex cases that clients are unable to handle on their own, usually assist clients on-site
Specialized in the most difficult cases, even the most complicated nation state-originated attacks |
Disrupting Reactive Cybersecurity Models with ChaosByCinthya Alaniz Salazar |Fri, 06/03/2022 - 10:31
The traditional, signature-based threat identification approach to cybersecurity leaves companies at a reactionary disadvantage. The Mathematical Chaos model breaks with the classical detection and response approach to cybersecurity, pushing forward a highly sensitive zero-trust model that is continuously reacting to anomalies in real-time, said Sneer Rozenfled, CEO, Cyber 2.0.
“The cybersecurity world is based on a vulnerable biological model that intrinsically always places hackers one step ahead,” said Rozenfled.
Existing cybersecurity solutions that concentrate singularly on detection and response will always fail eventually because 100 percent detection is not possible given the continuous innovation of malicious software. Although this field has made significant progress with anomaly detection through behavioral analysis and deep-packet inspection, these tools fundamentally rely on the identification of malicious software before preventing its spread throughout an organization. In other words, the traditional reactionary approach is designed to fail in the face of an ever-evolving cybersecurity landscape, which is churning out increasingly sophisticated cybersecurity threats on a daily basis.
“The overarching objective of Cyber 2.0 is to shift the primary focus of cybersecurity from detection to containment, shutting down an invasion before it can spread an exact greater damage,” said Rozenfled.
The Mathematical Chaos model is based on the Zero Trust security model, which operates under the assumption it does not know where the next cybersecurity threat will emerge from. This algorithm essentially verifies every software that requests to interact with a system’s network, even when Cyber 2.0 is removed from the system. This model is more adept to protect computers, which function and communicate based on numbers “rather than the biological approach that attempts to protect them as a human body,” said Rozenfled. This approach is complemented with over capacities including network obscurement, security operation centers and forensic capabilities, but these are all secondary to the chaos algorithm that does most of the heavy lifting.
To prove the validity of its cybersecurity approach, Cyber 2.0 has invited over 5,500 white-hat hackers over four years from 30 countries to attack its system, giving them administrator passwords as a starting point. All of them have failed so far. |
Full Form of ICMP:
Internet Control Message Protocol
ICMP Full Form is Internet Control Message Protocol. In the suite of internet protocol, one of the primary protocols includes the ICMP or the Internet Control Message Protocol. The devices used in a network, such as routers use it for sending error messages. This protocol is also utilized for relaying query messages. The protocol number assignment assigned to it is one. The ICMP protocol is different from transport protocols like UDP and TCP. The ICMP is not used in employing the applications of the end-user network or exchanging data between different systems.
The messages of ICMP are utilized for control or diagnostic purposes. They are also generated when errors occur in IP operations. These errors are directed to the IP addresses where the packet originated. A router, for example, forwards an IP datagram. In the IP header, the TTL or the time to live field is decreased by 1. If due to this reduction, the TTL becomes zero, the ICMP message of TTL is sent to the source address of the datagram, and the packet, in turn, is discarded.
The standard packets of IP consist of the ICMP messages. The processing of ICMP messages differs from the normal processing of IP. In the majority of the cases, the contents present in the ICMP message are inspected. The application that transmitted the IP packet is sent an error message. The base of numerous network utilities that are commonly used lies in the ICMP messages. The IP datagram can be transmitted with the header files of the IP TTL for implementing the trace route command. The ICMP messages like ‘Echo reply’ and ‘Echo request’ are used for implementing the related ping utility. |
[Snort-users] Sending syslog alerts from Snort on ArchLinux on RPI b+
bg31bf at ...17126...
Mon Mar 23 14:45:25 EDT 2015
Im issuing the command snort -d -h 192.168.1.0/24 -c /etc/snort/snort.conf
-s and on the syslog server i have syslog watcher 4.7.4 on windows 7. Then
i set up a rule for rules.conf file to alert ICMP packets. When I ping from
the windows machine to the Raspberry Pi the ICMP traffic is reported within
the console if snort is ran with the -A console option. But when the -s
option is selected it doesnt send alerts to the Syslog server. I did
configure the snort.conf in the syslog section with the IP address and 514
port of the Syslog server still no dice.
Am I missing something?
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More information about the Snort-users |
According to experts at the WordPress security plugin WordFence, attackers are using automated scans to target freshly installed WordPress websites, taking advantage of administrators who fail to properly configure their server’s settings. The experts dubbed the attack WPSetup attack.
Hackers launched thousands of scans each day, searching for the URL /wp-admin/setup-config.php, that new WordPress installs use to setup new sites.
The attackers aim to find new WordPress installs that are not yet configured by the administrators.
In the period between the end of May and mid-June, WordFence researchers observed a spike in the number of attacks targeting WordPress accounts from the end of May to mid-June.
“In May and June, we saw our worst-of-the-worst IPs start using a new kind of attack targeting fresh WordPress installations.” states WordFence.
“We also had our first site cleaning customer that was hit by this attack.
Attackers scan for the following URL:
This is the setup URL that new installations of WordPress use. If the attacker finds that URL and it contains a setup page, it indicates that someone has recently installed WordPress on their server but has not yet configured it. At this point, it is very easy for an attacker to take over not just the new WordPress website, but the entire hosting account and all other websites on that hosting account.”
In just one day, on May 30, the experts observed roughly 7,500 scans a day, a peak in the malicious activity.
The WPSetup attack leverages on the fact that a user hasn’t finished setting up its WordPress installation, the attacker can exploit this condition to complete the user’s installation.
The attackers operate with admin access, this means that they can enter their own database name, username, password, and database server. The attackers can take over the website running their own installation or creating a supplementary account.
How the WPSetup Attack Gets Full Control of Your Hosting Account?
Once the attacker gains admin access to a WordPress website running on your hosting account, they can execute PHP code via a theme or plugin editor.
The attackers can install a shell in a victim’s directory to access any files or websites on the account or access any databases or application data.
“Once an attacker can execute code on your site, they can perform a variety of malicious actions. One of the most common actions they will take is to install a malicious shell in a directory in your hosting account. At that point they can access all files and websites on that account. They can also access any databases that any WordPress installation has access to, and may be able to access other application data.” continues the analysis.
WordFence explained that the WPSetup attack is not new, but this is the first time for such kind of attack on a large-scale.
WordFence recommends users to create a specially coded .htaccess file in the base of their web directory to avoid attackers access it before the installation is completed.
“Before you install a fresh WordPress installation, create a .htaccess file in the base of your web directory containing the following:
order deny,allow deny from all allow from <your ip>"
Replace the ‘<your ip>’ with your own IP address. You can find this out by visiting a site like whatsmyip.org.
This rule ensures that only you can access your website while you are installing WordPress. This will prevent anyone else from racing in, completing your installation and taking control of your hosting account by uploading malicious code.
Once complete, you can remove the .htaccess rule and allow the rest of the world to access your website.”
(Security Affairs – WPSetup attack, WordPress) |
ThreatModeler™ provides a collaborative environment between various stakeholders including Architects, Developers, Security and Project Managers to identify threats in the requirements/architecture phase with little or no knowledge of security. It establishes a repeatable and scalable platform and provides security approved coding guidelines to build security within the application. Below are some of the key features of ThreatModeler™.
ThreatModeler™ includes a comprehensive library of threats including MITRE CAPEC library and other open vulnerability databases as well as research at MyAppSecurity to cover latest attack vectors that are not yet updated in other libraries.
The implementation of automatically generated attack trees, a threat management console and visualization of inter-component data flow helps you identify high value targets and how they can be attacked.
ThreatModeler’s intelligent threat engine (ThreatSense), identifies threats automatically based on the information provided and presents mitigation strategies to the development teams which can be easily integrated in their code.
ThreatModeler™ makes it easy to scale security initiatives in the fast-paced nature of software development by automatically analyzes threats in any new feature added to the application and providing mitigating solutions.
ThreatModeler™ integrates easily with any development methodology. It’s extensible and feature rich modules saves substantial time and effort in identifying threats and achieving the goal of building security in with minimum effort. |
What is Ransom:Win32/Paradise.BC!MTB infection?
In this article you will locate concerning the interpretation of Ransom:Win32/Paradise.BC!MTB and its unfavorable effect on your computer system. Such ransomware are a kind of malware that is clarified by on-line scams to demand paying the ransom by a target.
In the majority of the situations, Ransom:Win32/Paradise.BC!MTB infection will certainly instruct its targets to start funds move for the objective of counteracting the changes that the Trojan infection has actually presented to the target’s gadget.
These alterations can be as complies with:
- Executable code extraction;
- Creates RWX memory;
- Possible date expiration check, exits too soon after checking local time;
- Uses Windows utilities for basic functionality;
- Attempts to delete volume shadow copies;
- Attempts to restart the guest VM;
- Attempts to repeatedly call a single API many times in order to delay analysis time;
- Modifies boot configuration settings;
- Installs itself for autorun at Windows startup;
- Writes a potential ransom message to disk;
- Likely virus infection of existing system binary;
- Clears Windows events or logs;
- Anomalous binary characteristics;
- Uses suspicious command line tools or Windows utilities;
- Ciphering the documents situated on the victim’s hard disk — so the target can no longer use the data;
- Preventing regular accessibility to the target’s workstation;
One of the most common channels where Ransom:Win32/Paradise.BC!MTB Trojans are injected are:
- By means of phishing emails;
- As a repercussion of individual winding up on a resource that holds a destructive software application;
As soon as the Trojan is effectively injected, it will either cipher the data on the victim’s computer or protect against the device from functioning in an appropriate manner – while also putting a ransom note that points out the requirement for the targets to impact the repayment for the objective of decrypting the files or restoring the file system back to the preliminary problem. In the majority of instances, the ransom money note will turn up when the client restarts the COMPUTER after the system has currently been damaged.
Ransom:Win32/Paradise.BC!MTB circulation networks.
In various edges of the world, Ransom:Win32/Paradise.BC!MTB expands by jumps as well as bounds. Nonetheless, the ransom money notes as well as methods of obtaining the ransom money quantity might vary depending upon certain regional (local) settings. The ransom money notes as well as techniques of extorting the ransom money quantity may differ depending on certain neighborhood (local) setups.
As an example:
Faulty informs concerning unlicensed software application.
In certain areas, the Trojans typically wrongfully report having spotted some unlicensed applications allowed on the victim’s device. The alert after that demands the customer to pay the ransom.
Faulty declarations regarding prohibited content.
In countries where software piracy is much less preferred, this approach is not as effective for the cyber scams. Additionally, the Ransom:Win32/Paradise.BC!MTB popup alert might wrongly assert to be stemming from a police organization as well as will report having located kid porn or various other unlawful information on the tool.
Ransom:Win32/Paradise.BC!MTB popup alert might falsely claim to be deriving from a law enforcement establishment as well as will certainly report having located kid pornography or other prohibited data on the tool. The alert will in a similar way include a demand for the customer to pay the ransom.
File Info:crc32: A80CF34Fmd5: 567204cbb8d1c5908a5316f9dfdcb353name: 567204CBB8D1C5908A5316F9DFDCB353.mlwsha1: cc7eca3c24883a3b563288c08cfab7cc248a0315sha256: 54f6ec27eb7526c439d33e7592e4864842fccf950d828fe14ef7c8eb080ee371sha512: ec4e2a03a525ae5150449d5403f2fc72b88d1cd977c503f4943b0889b82c543e46c35cd204fe27c5c03d4817bcc9413ec467637a038d2d7cd164d59d2b377f3bssdeep: 6144:NICjjI4WHB/8cQoASA0AVjq6g0uhq+r0+K248Bb+MNa:ai6hEcQoA50sbuPq24EbJtype: PE32 executable (GUI) Intel 80386, for MS Windows
Version Info:0: [No Data]
Ransom:Win32/Paradise.BC!MTB also known as:
|Elastic||malicious (high confidence)|
|K7AntiVirus||Trojan ( 00574a961 )|
|K7GW||Trojan ( 00574a961 )|
|Sophos||Mal/Generic-R + Mal/EncPk-APW|
|SentinelOne||Static AI – Malicious PE|
|MAX||malware (ai score=100)|
|Cynet||Malicious (score: 100)|
|ESET-NOD32||a variant of Win32/Kryptik.HIJC|
How to remove Ransom:Win32/Paradise.BC!MTB virus?
Unwanted application has ofter come with other viruses and spyware. This threats can steal account credentials, or crypt your documents for ransom.
Reasons why I would recommend GridinSoft1
There is no better way to recognize, remove and prevent PC threats than to use an anti-malware software from GridinSoft2.
Download GridinSoft Anti-Malware.
You can download GridinSoft Anti-Malware by clicking the button below:
Run the setup file.
When setup file has finished downloading, double-click on the setup-antimalware-fix.exe file to install GridinSoft Anti-Malware on your system.
An User Account Control asking you about to allow GridinSoft Anti-Malware to make changes to your device. So, you should click “Yes” to continue with the installation.
Press “Install” button.
Once installed, Anti-Malware will automatically run.
Wait for the Anti-Malware scan to complete.
GridinSoft Anti-Malware will automatically start scanning your system for Ransom:Win32/Paradise.BC!MTB files and other malicious programs. This process can take a 20-30 minutes, so I suggest you periodically check on the status of the scan process.
Click on “Clean Now”.
When the scan has finished, you will see the list of infections that GridinSoft Anti-Malware has detected. To remove them click on the “Clean Now” button in right corner.
Are Your Protected?
GridinSoft Anti-Malware will scan and clean your PC for free in the trial period. The free version offer real-time protection for first 2 days. If you want to be fully protected at all times – I can recommended you to purchase a full version:
If the guide doesn’t help you to remove Ransom:Win32/Paradise.BC!MTB you can always ask me in the comments for getting help.
User Review( votes) |
$ curl <router_service_IP> <port>
The OpenShift Container Platform egress router pod redirects traffic to a specified remote server from a private source IP address that is not used for any other purpose. An egress router pod can send network traffic to servers that are set up to allow access only from specific IP addresses.
The egress router pod is not intended for every outgoing connection. Creating large numbers of egress router pods can exceed the limits of your network hardware. For example, creating an egress router pod for every project or application could exceed the number of local MAC addresses that the network interface can handle before reverting to filtering MAC addresses in software.
The egress router image is not compatible with Amazon AWS, Azure Cloud, or any other cloud platform that does not support layer 2 manipulations due to their incompatibility with macvlan traffic.
In redirect mode, an egress router pod configures
iptables rules to redirect traffic from its own IP address to one or more destination IP addresses. Client pods that need to use the reserved source IP address must be configured to access the service for the egress router rather than connecting directly to the destination IP. You can access the destination service and port from the application pod by using the
curl command. For example:
$ curl <router_service_IP> <port>
In HTTP proxy mode, an egress router pod runs as an HTTP proxy on port
8080. This mode only works for clients that are connecting to HTTP-based or HTTPS-based services, but usually requires fewer changes to the client pods to get them to work. Many programs can be told to use an HTTP proxy by setting an environment variable.
In DNS proxy mode, an egress router pod runs as a DNS proxy for TCP-based services from its own IP address to one or more destination IP addresses. To make use of the reserved, source IP address, client pods must be modified to connect to the egress router pod rather than connecting directly to the destination IP address. This modification ensures that external destinations treat traffic as though it were coming from a known source.
Redirect mode works for all services except for HTTP and HTTPS. For HTTP and HTTPS services, use HTTP proxy mode. For TCP-based services with IP addresses or domain names, use DNS proxy mode.
The egress router pod setup is performed by an initialization container. That container runs in a privileged context so that it can configure the macvlan interface and set up
iptables rules. After the initialization container finishes setting up the
iptables rules, it exits. Next the egress router pod executes the container to handle the egress router traffic. The image used varies depending on the egress router mode.
The environment variables determine which addresses the egress-router image uses. The image configures the macvlan interface to use
EGRESS_SOURCE as its IP address, with
EGRESS_GATEWAY as the IP address for the gateway.
Network Address Translation (NAT) rules are set up so that connections to the cluster IP address of the pod on any TCP or UDP port are redirected to the same port on IP address specified by the
If only some of the nodes in your cluster are capable of claiming the specified source IP address and using the specified gateway, you can specify a
nodeSelector to identify which nodes are acceptable.
An egress router pod adds an additional IP address and MAC address to the primary network interface of the node. As a result, you might need to configure your hypervisor or cloud provider to allow the additional address.
If you deploy OpenShift Container Platform on RHOSP, you must allow traffic from the IP and MAC addresses of the egress router pod on your OpenStack environment. If you do not allow the traffic, then communication will fail:
$ openstack port set --allowed-address \ ip_address=<ip_address>,mac_address=<mac_address> <neutron_port_uuid>
If you are using RHV, you must select No Network Filter for the Virtual network interface controller (vNIC).
If you are using VMware vSphere, see the VMware documentation for securing vSphere standard switches. View and change VMware vSphere default settings by selecting the host virtual switch from the vSphere Web Client.
Specifically, ensure that the following are enabled:
To avoid downtime, you can deploy an egress router pod with a
Deployment resource, as in the following example. To create a new
Service object for the example deployment, use the
oc expose deployment/egress-demo-controller command.
apiVersion: apps/v1 kind: Deployment metadata: name: egress-demo-controller spec: replicas: 1 (1) selector: matchLabels: name: egress-router template: metadata: name: egress-router labels: name: egress-router annotations: pod.network.openshift.io/assign-macvlan: "true" spec: (2) initContainers: ... containers: ...
|1||Ensure that replicas is set to |
Manage Device Groups
- Add a Device Group
- Create a Device Group Hierarchy
- Create Objects for Use in Shared or Device Group Policy
- Revert to Inherited Object Values
- Manage Unused Shared Objects
- Manage Precedence of Inherited Objects
- Move or Clone a Policy Rule or Object to a Different Device Group
- Select a URL Filtering Vendor on Panorama
- Push a Policy Rule to a Subset of Firewalls
- Manage the Rule Hierarchy
Device Group Objects
Device Group Objects Objects are configuration elements that policy rules reference, for example: IP addresses, URL categories, security profiles, users, services, and applications. Rules of ...
Device Group Hierarchy
Device Group Hierarchy You can Create a Device Group Hierarchy to nest device groups in a tree hierarchy of up to four levels, with lower-level ...
Manage Precedence of Inherited Objects
Manage Precedence of Inherited Objects By default, when device groups at different levels in the Device Group Hierarchy have an object with the same name ...
Create a Device Group Hierarchy
Create a Device Group Hierarchy Plan the Device Group Hierarchy . Decide the device group levels, and which firewalls and virtual systems you will assign ...
Device Groups To use Panorama effectively, you have to group the firewalls in your network into logical units called device groups . A device group ...
Override or Revert an Object
Override or Revert an Object In Panorama, you can nest device groups in a tree hierarchy of up to four levels. At the bottom level, ...
Create Objects for Use in Shared or Device Group Policy
Create Objects for Use in Shared or Device Group Policy You can use an object in any policy rule that is in the Shared location, ...
Manage the Rule Hierarchy
Manage the Rule Hierarchy The order of policy rules is critical for the security of your network. Within any policy layer (shared, device group, or ...
Plan Your Multi-NSX Deployment
Plan Your Multi-NSX Deployment You must carefully plan your device group hierarchy and template stacks and consider how they interact with the other components needed ... |
For testing, your web application might be protected using (HTTP Basic) authentication, prompting you to enter a username and password to access the site.
Ghostlab can help you in this case (only if HTTP Basic Authentication is used, though): you can add the username and password in Ghostlab so it sends the credentials to the server and doesn’t prompt you in the actual browsers any longer.
To do this,
- go to the Site Settings and locate the HTTP Headers section in the advanced settings section;
- click Add Auth Header at the bottom of the table;
- a new row will appear with “Username” and “Password” fields to the right; enter your credentials there. |
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