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Using the neoVI API in Excel - neoVI API To use the intrepidcs API in Excel or other VBA supported application add the bas_neoVI.vbmodule into your project (figure 1) by right clicking on the Project in the VBA Editor and selecting Insert and then Module. Open the bas_neoVI.vb. Then, call the methods as defined in the Basic Operation document. The function calls for use in VBA are the same as the calls in Visual Basic 6. Figure 1 - Add Module Command From the VB.NET Menu. A VBA Excel example (Figure 1) is included to show how the API all works together. This project only has 1 file, NeoVIExample.XlS. Make sure macros are enabled to run this example. All the needed project files are included in the following file: The example shows how to open and close communication to the driver, send messages and read messages on the networks. Figure 2 - The VBA Excel Example.
I recently finished my master’s degree. Now I am sharing my dissertation so other people can continue the work I started. Secure programming is the practice of writing programs that are resistant to attacks by malicious people or programs. Programmers of secure software have to be continuously aware of security vulnerabilities when writing their program statements. They also ought to continuously perform actions for preventing or removing vulnerabilities from their programs. In order to support these activities, static analysis techniques have been devised to find vulnerabilities in the source code. However, most of these techniques are built to encourage vulnerability detection a posteriori, only when developers have already fully produced (and compiled) one or more modules of a program. Therefore, this approach, also known as late detection, does not support secure programming but rather encourages posterior security analysis. The lateness of vulnerability detection is also influenced by the high rate of false positives, yielded by pattern matching, the underlying mechanism used by existing static analysis techniques. The goal of this dissertation is twofold. First, we propose to perform continuous detection of security vulnerabilities while the developer is editing each program statement, also known as early detection. Early detection can leverage his knowledge on the context of the code being created, contrary to late detection when developers struggle to recall and fix the intricacies of the vulnerable code they produced from hours to weeks ago. Our continuous vulnerability detector is incorporated into the editor of an integrated software development environment. Second, we explore a technique originally created and commonly used for implementing optimizations on compilers, called data flow analysis, hereinafter referred as DFA. DFA has the ability to follow the path of an object until its origins or to paths where it had its content changed. DFA might be suitable for finding if an object has a vulnerable path. To this end, we have implemented a proof-of-concept Eclipse plugin for continuous vulnerability detection in Java programs. We also performed two empirical studies based on several industry-strength systems to evaluate if the code security can be improved through DFA and early vulnerability detection. Our studies confirmed that: (i) the use of data flow analysis significantly reduces the rate of false positives when compared to existing techniques, without being detrimental to the detector performance, and (ii) early detection improves the awareness among developers and encourages programmers to fix security vulnerabilities promptly. If you want to read the whole dissertation, you can download it here. I would love to receive your feedback about it. Early detection; security vulnerability; data flow analysis; secure programming.
The keepers of Kubernetes, the rather popular software container orchestration system, have pushed out three new releases that patch a critical flaw. In a post to the Kubernetes announcement list on Monday, Google senior staff engineer Jordan Liggitt says Kubernetes version v1.10.11, v1.11.5, and v1.12.3 have been made available to fix CVE-2018-1002105, a privilege escalation vulnerability. The code error in the open source project has been designated severity 9.8 out of 10 because it can be executed remotely, the attack is not complex and no user interaction or special privileges are required . According to Liggitt, a malicious user could use the Kubernetes API server to connect to a backend server to send arbitrary requests, authenticated by the API server's TLS credentials. The API server is the main management entity in Kubernetes. It talks to the distributed storage controller etcd and to kublets, the agents overseeing each node in a cluster of software containers. The bug was spotted by Darren Shepherd, chief architect and co-founder at Rancher Labs. Red Hat OpenShift, an enterprise-oriented container platform, has introduced patches for all product variants. "This is a big deal," said Ashesh Badani, veep and general manager of OpenShift at Red Hat, in a blog post. "Not only can [miscreants] steal sensitive data or inject malicious code, but they can also bring down production applications and services from within an organization’s firewall." There are two primary attack vectors. Using the first, an individual possessing the Pod exec/attach/portforward privileges granted to a normal user by default can become a cluster-admin, thereby gaining access to any container in the Pod and potentially any information therein. The second method lets an unauthenticated user access the API to create unapproved services, which could be used to inject malicious code. "Any unauthenticated user with access to a Kubernetes environment can hit the discovery endpoint which proxies the aggregated API server (not the kube-apiserver)," explained Christopher Robinson, manager of product security assurance at Red Hat, in an email to The Register. "Crafting a message to the API so that an upgrade fails can leave the connection alive and allows re-use with arbitrary headers, and then allows cluster-admin level access to that aggregated API server. This could be used against the service-catalog that would allow for the creation of arbitrary service instances." The vulnerability is particularly troubling because any unauthorized requests cannot be easily detected. According to Liggitt, they do not show up in the Kubernetes API server audit logs or server log. Malicious requests are visible in kublet or aggregated API server logs, but there's nothing that distinguishes them from authorized and proxied requests via the Kubernetes API server. ®
The statusbar displays informational messages. In general, the left side will show context related information, the middle part will show information about the current capture file, and the right side will show the selected configuration profile. Drag the handles between the text areas to change the size. This statusbar is shown while no capture file is loaded, e.g. when Wireshark is started. The middle part shows the current number of packets in the capture file. The following values are displayed: For a detailed description of configuration profiles, see Section 10.6, “Configuration Profiles”. This is displayed if you have selected a protocol field from the “Packet Details” pane. The value between the parentheses (in this example ‘ipv6.src’) can be used as a display filter, representing the selected protocol field. This is displayed if you are trying to use a display filter which may have unexpected results. For a detailed description, see Section 6.4.6, “A Common Mistake”.
The top four cloud IT security misconfigurations and how to fix them Article by ExtraHop A/NZ regional sales manager Glen Maloney. In recent years, reports of large-scale data breaches have become depressingly common. Banks, healthcare providers, retailers and governments have all become targets of cybercriminals looking to extract data or cause disruption. At the dawn of a new decade, this situation is likely only to get worse. With the value of data increasing by the day, its appeal to criminals will continue to grow. Another contributing factor is the increasing usage by organisations of Infrastructure as a Service (IaaS) platforms. In recent years, research by security firm McAfee shows almost 70% of data record breaches (a total of 5.4 billion) were caused by unintentional internet exposure due to the misconfiguration of services and portals. As the research found, the vast majority of these misconfigurations go unreported and often even unnoticed. This means the problem is likely to be even larger than people might think. Thankfully, there are some effective steps that can be taken to overcome four of the most common security issues, thereby reducing the attack surface. The top four misconfigurations issues are: 1. No restrictions on outbound access To ensure effective IT security, outbound data traffic from a cloud platform should always be configured using the principle of ‘minimalist authority’. Many users of cloud platforms tend to only configure inbound ports in security groups and don’t pay the same attention to outbound ports. However, limiting outbound traffic can ensure data is only made available to the applications and users that are authorised to use it. By doing this, the security team can reduce the risk of internal network scans and lateral movement. 2. Failure to restrict access to non-HTTP and HTTPS ports While web servers are primarily designed to host websites and web services exposed to the internet, they are also able to handle services such as SSH or RDP for management or databases. However, if this is the case, it becomes vital to block access to them from the public internet. If the ports are left incorrectly or improperly configured, an organisation can find itself open to attackers prepared to use brute force to gain access to systems. If, for some reason, these ports are opened to the internet, it’s vital to ensure they are configured to only accept traffic from particular, pre-determined IP addresses. 3. Not placing restrictions on inbound access on seldom-used ports It’s often said that ‘security through obscurity’ never really works. Some services running within an IT infrastructure use high-numbered TCP or UDP ports to obfuscate what is running, but it doesn’t do much to improve security. It certainly won’t offer any protection from a determined cybercriminal looking for access. The use of high-level ports should always be carefully restricted so that only necessary systems have access. 4. Having unrestricted ICMP Access Experience shows that ICMP is a useful networking protocol, however if it is left open to the internet it can open an organisation to some rather straightforward attacks. One of the most common uses of ICMP is to use ICMP Echo to verify that servers are online and responsive. While ICMP Echo is an excellent diagnostic tool for security teams, it's also a popular tool among cybercriminals. A simple ping scan of the internet, using Nmap or Fping, can alert criminals to the fact that there is a server online with the potential to be attacked. Attackers can also use ICMP in other ways. For example, a ping flood can be created that overwhelms a server with too many ICMP messages and creates an effective Denial of Service (DoS) attack. It’s best to make blocking ICMP part of regular security activity. The increasing role of Network Detection and Response (NDR) As more organisations take advantage of cloud services and resources, the complexity of their infrastructures will continue to increase. As this complexity rises, so too does the challenge of keeping it secure. Indeed, while the ability to quickly build servers and services on a cloud platform delivers significant operational advantages, it also brings with it some big security risks. In a complex environment, it’s easy to miss a single setting or configuration option that can open the entire infrastructure to attack. One of the biggest reasons that security for cloud platforms has lagged behind that of more traditional on-premise infrastructures has been that it has been very difficult to capture and parse network traffic in the cloud. Thankfully, this situation is now changing. Increasing numbers of organisations are discovering that it’s possible to monitor network communications in real time by using a Network Detection and Response (NDR) tool. They find it’s the is the easiest way to stay on top of complex and dynamic IT environments that include cloud-based components. Consider whether putting a NDR tool to work within your infrastructure might allow you to enjoy the advantages of the cloud without having to contend with complex and unwieldy security requirements.
Inter-Subnet Routing Traffic Flow For a BGP EVPN VXLAN network, symmetric Integrated Routing and Bridging (IRB) is used to forward data traffic. In a BGP EVPN VXLAN fabric, the same VRF-to-L3VNI mapping must be present on every edge device or VTEP where that VRF is configured. The procedure for forwarding routed traffic over VXLAN is very similar to routing operations in non-VXLAN environments. Below topology is used to discuss the Routing traffic flow. Four endpoints (Host A, Host B, Host C, Host D) residing in VRF A associated with L3VNI 50002 with further VNI 20001 is associated with IP subnet 192.168.1.0/24, on which endpoints Host A and Host C reside, and VNI 20002 is associated with IP subnet 192.168.2.0/24, on which endpoints Host B and Host D reside. Before Routing happens, BGP control plane information must be populated and IP/MAC information about the endpoints is distributed using BGP route type 2 messages. Likewise, the subnet prefix information is distributed using BGP route type 5 messages. To Verify from BGP CLI use the following command to verify the able table. Once Control Plane is populated now it’s time for data traffic to flow. Now when host A wants to talk to Host B or Host D which is in different subnet, Host A will Send ARP request to get the Mac address of its gateway (MAC of VLAN 10). Step1: ARP Request, from Host A to Distributed IP Anycast Gateway Host A initiates an ARP request for the IP address of its default gateway. At VTEP V1, the ARP request is then evaluated through ARP snooping, and the retrieved source information is populated in the BGP EVPN control protocol. Host A’s MAC 0000.3100.1001 and IP 192.168.1.11 then becomes known as behind VTEP V1. The ARP reply sent from the distributed IP anycast to endpoint Host A. Once this information is received, Host A updates its ARP cache with the AGM 3030.0000.00BB mapped to the default gateway IP entry 192.168.1.1. Now, endpoint Host A is ready to communicate with other endpoints in different subnets.
In malware accident investigation, the most important thing is detection of malicious code. Signature based anti-virus software have been used in most of the accident. Malware can easily avoid signature based detection by using packing or encryption method. Because of this, packed file detection is also important. Detection methods can be divided into signature based detection and entropy based detection. Signature based detection can not detect new packing. And entropy based detection has a problem with false positive. We provides detection method using entropy statistics of the entry point section and 'write' properties of essential characteristic of packed file. And then, we show packing detection tool and evaluate its performance.
Malware, phishing emails, and even rigged attachments used in an attack contain some obvious and some hidden clues that can be used to glean intel on who's behind the attack and what they are after -- perhaps an APT looking for intellectual property, or a cybercrime gang trying to pilfer financial information, according to FireEye, which today published a rundown of seven elements of malware that can provide valuable intel and insight on the attackers behind a hack and their motives. "More and more organizations are under attacks by targeted intrusion groups," says Alex Lanstein, network and systems architect for FireEye. "A lot of people who don't work for malware companies don't know what to look for ... We thought it would be [helpful] for them to understand our workflow." That doesn't mean that every targeted organization should -- or even can -- conduct CSI-style investigations into malware using clues left behind. Those types of operations are best left to seasoned forensic investigators and researchers, security experts say. But even so, any clues an organization can glean from an attack can help jump-start a more thorough forensics investigation. The keyboard layout, malware metadata, embedded fonts, DNS registration, written language, remote administration tools (RATs), and behavior patterns all can provide clues to ID attackers, according to FireEye, which built the seven-item checklist from some 1,500 attack campaigns it tracks. The biggest advantage to gathering this intel is being able to discern whether an attack is targeted or opportunistic, Lanstein says. "It's really about tying together multiple indicators, not just one indicator," he says. And FireEye warns that attack clues can easily be misleading, so it's best to get help from forensic experts and not take any clue at face value. [How naming names of hackers and pinpointing the beneficiaries of cyberspying and cybercrime attacks translate into a new kind of defense. See Turning Tables: ID'ing The Hacker Behind The Keyboard .] "Those are the basics you start with," says Dmitri Alperovitch, CTO at CrowdStrike, whose strategy is getting to the bottom of the attacker or group behind an advanced attack rather than on the malware. "We look at the tradecraft, what the attacker is trying to do, [for example]. If you're only looking at malware, everyone is using PoisonIvy, so it's going to be hard to get attribution just on that." The key is getting beyond concluding the attacker is Chinese, and determining what organization the hacker or hackers belong to and what their strategies are. "Then you can get predictive about what types of victims they are going after," for example, Alperovitch says. FireEye says the attacker's keyboard layout can reveal where he's from and what language he speaks -- it's easy to spot a Mandarin (GB2312) keyboard used in China -- as can language artifacts found in malware. Even just tracing broken English in a phishing email to a Google translation can pinpoint the attacker's native tongue, FireEye says. The malware's own metadata can be telling, too: It contains technical information on location and language, and could lead to connections to other attack campaigns. FireEye found source code that it ultimately traced to the Chinese APT1/Comment Group after studying a file in the malware that turned out to be a variant of the WEBC2 malware used by the group. Even the fonts used in phishing messages can contain clues about the origin of the attack or the attacker's native tongue: In the recently revealed Sanny APT attack, for instance, the rigged document was written in Russian. But it actually used two Korean fonts: "Those font choices reconfirmed existing evidence from other sources that pointed to North Korea, including the author's name and the CnC servers used in the attack. Taken together, the evidence presented a convincing case as to the attacker's origins," FireEye said in its report. Other elements, such as DNS registration information, can help tie an attacker to a location, and the type of RAT used by the attacker can often help ID him. Then there are the behavioral patterns used by attackers: They often pick the same targets and industries, and employ the same command-and-control servers, according to FireEye. "In the same way, the attacker's exploit toolkits and tactics also help profile the attacker," and can reveal multiple attackers from the same group using the same infrastructure, according to FireEye. CrowdStrike's Alperovitch notes that while attack indicators can change, the attackers' tradecraft remains mostly the same, such as "how they craft their communications protocols, how they remain stealthy within a company once they hit a company." "You have to put yourself in the shoes of the attacker. They do hundreds of ops a week. They are not physically able to do everything different for every attack," he says. So there are clues that can be gleaned, he adds. The full FireEye report, "Digital Bread Crumbs: Seven Clues To Identifying Who's Behind Advanced Cyber Attacks," including screen shots of malware and attack clues, is available here (PDF) for download. Have a comment on this story? Please click "Add Your Comment" below. If you'd like to contact Dark Reading's editors directly, send us a message.
Source: Barricade Blog Twitter @barricadeio New malware is found which is infecting into the WordPress. The Mal/Badsrc-c is detected in the index.html, which would display itself as the Microsoft internet explorer browser. The malware is capable of injecting itself into the PHP code, which is used in some websites running WordPress, spreading itself effectively. According to UK security vendor Sophos, the malicious code spreads only if the user agents, uses the internet explorer browser.
Web Security by Using a Protective Technology: Cloud Based Service Having been on the wane in recent years, malware attacks are on the rise again. Malware is a term that refers to malicious software that is designed to cause damage to computer networks. The term covers a variety of programs including viruses, worms, Trojans, spy ware, aware, crime ware and root kits. Social networking sites in particular are proving to be an increasingly popular target for hackers, phishers and spammers looking to distribute malware. In terms of web-based threats, many organizations are leaving protection against malware threats to chance. In this paper, the authors are going to present web security by using cloud based service that protects organizations against crimeware and malware entering their organization.
Please use this identifier to cite or link to this item: \|Title:\|\|A Framework for Improving Attack Detection Accuracy using Ensemble Methods\| \|Keywords:\|\|IDS;Attacks;Classification;Boosting;Bagging;Ensemble methods;Naive bayes;CSED\| \|Abstract:\|\|With the development of internet and communication cyber movement started into new era. Today internet is used by all the organizations and people and they share a lot of sensitive information. A lot of attacks occur through the internet. These attacks exploit the vulnerabilities present in the system and may destroy the sensitive information present in the system. Protection mechanisms need to be provided to protect against these attacks. Firewalls and other basic security measures have been implemented to counter these attacks but these have failed as everyday novel ideas are developed by attackers to attack the system. Thus there is need to develop a system to eradicate these attacks as they damage the confidential information of the organizations. Intrusion detection systems are used for this purpose. Data mining can be used in case of intrusion detection system to differentiate between legitimate and illegitimate connections. Various classification algorithms can be applied that classify the connection either as normal or of specific attack type. Ensemble learning techniques are being currently considered as a new way to detect intrusive activities in systems as they have higher accuracy. The proposed approach is fusion of classification with boosting algorithms. In ensemble learning fusion of two or more techniques is done and accuracy of the combined system is large as compared to the individual techniques. The proposed model is applied on KDDCUP’99 dataset i.e. widely available dataset for intrusion detection systems. The results of the individual classification algorithms are compared with ensembling results. The ensemble learning better classifies the results to their proper category in terms of accuracy and number or instances properly classified.\| \|Description:\|\|M.E. (Information Security) CSED\| \|Appears in Collections:\|\|Masters Theses@CSED\| Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
International Journal of Applied Research in Computing With the changes in computing environment, the research on mobile agent has attracted wide range of applications. Migration strategy of mobile agent becomes one of the popular research topics. Its paradigm has attracted many attentions but it is still not widely used. The reason for this is that it suffers from the various issues regarding reliable mechanisms like security and the fault tolerance in mobile agent system. Since mobile agent moves from one server to another in an itinerary it is easily prone to various faults like server crash or agent crash, which makes fault tolerance one of the main issues of reliability in mobile agent system.
Warning! A new Ransomware strain that also performs denial of service attacks on your other devices has been discovered. Hi there again my fellow Nexthinkers. Today I completed some more Ransomware research. Instead of just being satisfied that they have encrypted all your files on network shares and locking your machines, this variant of Ransomware also started a DoS attack that blasts SPOOFED network traffic at various IP addresses. This is the first Ransomware I have seen with DDoS capability. This Ransomware uses a file-less attack method “Weaponised documents” which most antivirus and “next-gen” antivirus software’s are not capable of seeing inside a phishing email with a random filename. In this case the Phishing email exploited their victims using Visual Basic in a Rich Text Document (.rtf), this in turn launched Microsoft Word and initiated the embedded macros. The macros then ran an administrator command prompt on the host to create another malicious binary with random code which is then executed. The observed network traffic looks to be flooding the subnet with UDP port 6892 and would spoof the source. Since the source of the DoS attack will be spoofed, finding the real source of the infected machine will be very quick in Nexthink minimising downtime by just searching for any endpoints using UDP 6829. It has the potential capability to join a Botnet to be used in a Distributed Denial of Service attack (DDoS) as a chargeable Dark Web service. Extra File Information As new variants of this come out with different hash values and knowing that traditional AV will not be able to keep up, please take advantage of our behavioral alerts and investigations to stop this Ransomware from impacting your business.
There are numerous methods to see if you have been hacked. Using the Command Prompt, you can run a command which will check for incoming and outgoing connections to your computer. By checking all the established connections, you can identify a malicious connection from a hacker. - Click “Start,” then type “cmd” into the quick search. Right-click the app link and click “Run as administrator.” - Type “netstat -an” and press “Enter. - Look through the results in the command-line tool “netstat” (network statistics). The first column tells you the type of connection, the second shows you the local address, the third the foreign address, and the final column shows the status of the connection. - Check the port numbers for any intrusion. The port numbers follow the IP or server address in the following format: “:XXXX.” Ports between 0 and 1023 are generally safe; you should be suspicious of ports between 1024 and 65535 as it is most likely an attacker will run malicious software on there. Certain peer-to-peer software applications use the final port range, so close any P2P software and scan again. - Check suspicious IP addresses by looking in the third column. The foreign address refers to the location of the connection destination. Use a search engine to search through these IPs to see if they match up with programs, such as Windows Live Messenger. If you do however, find a suspicious looking ip address , you can either trace him or ddos him yourself. Show him who’s in control and let the cyber war begin! Just kidding ,if you are living in a country with a cyber division ,i’d advise you to contact them for professional assistance. Other than that , you can leave a comment and i will gladly help in any way. 🙂
SSL Inspection and Google Consumer Apps When the SSL Inspection feature is enabled on the Barracuda Web Security Gateway, the administrator has granular control over what applications are blocked or allowed on websites like Facebook, Twitter or Google. In these use cases, administrators can typically apply block/allow policies by specifying domain/sub-domain patterns associated with the website to be inspected over HTTPS. However, with Google consumer apps, there are currently some limitations due to the way in which Google deals with SSL certificates. These limitations, and the Barracuda Networks solution for correct identification and filtering of Google domains and sub-domains over HTTPS, are addressed in this article. For instructions and examples on how to block Google consumer apps over HTTPS, see Google Workspace Control Over HTTPS. To filter Google consumer apps traffic for Chromebooks, see How to Get and Configure the Barracuda Chromebook Security Extension. The extension requires upgrading to the Barracuda Web Security Gateway version 11 or above. Google Restrictions on Identifying Google sub-domains Over HTTPS Google has been moving more of its services to encrypted (HTTPS) connections for additional security, and is tending towards moving all of their sites to use HTTPS by default. In some cases, when SSL inspecting web traffic to Google sites, the only information the Barracuda Web Security Gateway has to evaluate over the encrypted connection is the IP address and the certificate name, which in most cases, including Google, is a wildcard certificate (*.google.com) identifying the domain name but not the specific host. Additionally, many schools and other institutions still use older versions of Windows and various browsers. These issues result in limited ability to completely identify certain Google sub-domains, and to apply differentiated policies such as, for example: - Allow an encrypted connection to Google drive but block the connection to mail.google.com - Allow an encrypted connection to Google business accounts, but block the connection to Google consumer accounts Limited Abilities for Some Mobile Devices With SSL Inspection With the introduction of mobile devices and specialized apps such as the Google Play Store on Android or the Google Drive app for Windows, limitations in SSL inspecting this web traffic are also an issue due to varied support for SSL Inspection tools in these specialized apps. Some versions of the apps support SSL Inspection tools needed to specifically determine the identity of certain Google sites over HTTPS, and some do not. This means that some selective policies based on service can’t be applied to that web traffic These issues are causing difficulties for schools in applying granular policy to student web browsing and to other organizations when applying policies. Google is working to make changes on their side to resolve them. For schools that have not adopted Google Workspace for Education, and are not widely using Android-based mobile devices, these issues may not be a problem. Use the Barracuda Chromebook Security Extensionee How to Get and Configure the Barracuda Chromebook Security Extension. The extension requires upgrading to the Barracuda Web Security Gateway version 11 or above. Deploy the Barracuda Web Security Gateway in Proxy Mode This deployment allows for full access to the URL that the user is accessing and can fully identify the resource and make differentiated policy decisions. See Forward Proxy Deployment of the Barracuda Web Security Gateway. Use the Google Consumer Apps Category Filter - From the BLOCK/ACCEPT > Web App Control page, in the Allowed Applications box, select Google Consumer Apps under Category Filter. - In the list box, you can either select Google Mail or Google Consumer Apps, and click the Block button to move it to the Blocked Applications list box. Click Save.
IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 19, NO. 6, DECEMBER 2011 Topological Detection on Wormholes in Wireless Ad Hoc and Sensor Networks Dezun Dong, Member, IEEE, Mo Li, Member, IEEE, Yunhao Liu, Senior Member, IEEE, Xiang-Yang Li, Senior Member, IEEE, and Xiangke Liao Abstract—Wormhole attack is a severe threat to wireless ad hoc and sensor networks. Most existing countermeasures either require specialized hardware devices or make strong assumptions on the network in order to capture the speciﬁc (partial) symptom induced by wormholes. Those requirements and assumptions limit the applicability of previous approaches. In this paper, we present our attempt to understand the impact and inevitable symptom of wormholes and develop distributed detection methods by making as few restrictions and assumptions as possible. We fundamentally analyze the wormhole problem using a topology methodology and propose an effective distributed approach, which relies solely on network connectivity information, without any requirements on special hardware devices or any rigorous assumptions on network properties. We formally prove the correctness of this design in continuous geometric domains and extend it into discrete domains. We evaluate its performance through extensive simulations. Index Terms—Connectivity, topological approach, wireless ad hoc and sensor networks, wormhole detection. ORMHOLE attack is one of the most severe security threats – in ad hoc and sensor networks. In wormhole attacks, the attackers tunnel the packets between distant locations in the network through an in-band or out-of-band channel. The wormhole tunnel gives two distant nodes the illusion that they are close to each other. The wormhole can attract and bypass a large amount of network trafﬁc, and thus the attacker can collect and manipulate network trafﬁc. The attacker is able to exploit such a position to launch a variety of attacks, such as dropping or corrupting the relayed packets, that signiﬁcantly imperils a lot of network protocols including routing , , localization, etc. . This paper focuses Manuscript received October 07, 2009; revised May 16, 2010 and November 18, 2010; accepted March 29, 2011; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Editor D. Agrawal. Date of publication August 22, 2011; date of current version December 16, 2011. The work of D. Dong was supported in part by the NSFC under Grants 60903223 and 60903224. This work of M. Li was supported by COE_SUG/RSS_20Aug2010_13/14 in Nanyang Technological University of Singapore. The work of X.-Y. Li was supported in part by NSF CNS-0832120 and NSF CNS-1035894. D. Dong and X. Liao are with the School of Computer and the National Laboratory for Paralleling and Distributed Processing, National University of Defense Technology (NUDT), Changsha 410073, China (e-mail: dong@nudt. edu.cn; [email protected]). M. Li is with the Computer Science Division, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore (e-mail: [email protected]). Y. Liu is with the TNLIST, School of Software, Tsinghua University, Beijing 100084, China, and also with The Hong Kong University of Science and Technology, Kowloon, Hong Kong (e-mail: [email protected]). X.-Y. Li is with Department of Computer Science, Illinois Institute of Technology, Chicago, IL 61606 USA (e-mail: [email protected]). Digital Object Identiﬁer 10.1109/TNET.2011.2163730 on typical wormhole attacks. The adversary is an outsider who does not have valid network identity. The establishment of wormhole attacks is independent of the general security mechanisms employed in the network. The attacker can forward each bit of a communication stream over the wormhole directly without breaking into the content of packets. Thus, the attacker does not need to compromise any node and obtain valid network identities to become part of the network. Using the wormhole links, the... References: P. Papadimitratos and Z. J. Haas, “Secure routing for mobile ad hoc networks,” presented at the SCS CNDS, San Antonio, TX, Jan. 27–31, 2002. K. Sanzgiri, B. Dahill, B. Levine, and E. Belding-Royer, “A secure routing protocol for ad hoc networks,” in Proc. IEEE ICNP, 2002, pp. 78–87. Y.-C. Hu, A. Perrig, and D. Johnson, “Packet leashes: A defense against wormhole attacks in wireless networks,” in Proc. IEEE INFOCOM, 2003, vol. 3, pp. 1976–1986. IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 19, NO. 6, DECEMBER 2011 L. Hu and D. Evans, “Using directional antennas to prevent wormhole attacks,” presented at the NDSS, 2004. W. Wang and B. Bhargava, “Visualization of wormholes in sensor networks,” in Proc. ACM WiSe, 2004, pp. 51–60. W. Wang, B. Bhargava, Y. Lu, and X. Wu, “Defending against wormhole attacks in mobile ad hoc networks,” Wireless Commun. Mobile Comput., vol. 6, pp. 483–503, 2006. J. Eriksson, S. V. Krishnamurthy, and M. Faloutsos, “Truelink: A practical countermeasure to the wormhole attack in wireless networks,” in Proc. IEEE ICNP, 2006, pp. 75–84. R. Poovendran and L. Lazos, “A graph theoretic framework for preventing the wormhole attack in wireless ad hoc networks,” Wireless Netw., vol. 13, pp. 27–59, 2007. R. Maheshwari, J. Gao, and S. R. Das, “Detecting wormhole attacks in wireless networks using connectivity information,” in Proc. IEEE INFOCOM, 2007, pp. 107–115. S. Capkun, L. Buttyan, and J.-P. Hubaux, “Sector: Secure tracking of node encounters in multihop wireless networks,” in Proc. ACM SASN, 2003, pp. 21–32. I. Khalil, S. Bagchi, and N. B. Shroff, “Liteworp: A light-weight countermeasure for the wormhole attack in multihop wireless networks,” in Proc. DSN, 2005, pp. 612–621. I. Khalil, S. Bagchi, and N. B. Shroff, “Mobiworp: Mitigation of the wormhole attack in mobile multihop wireless networks,” in Proc. IEEE SecureComm, 2006, pp. 1–12. N. Song, L. Qian, and X. Li, “Wormhole attack detection in wireless ad hoc networks: A statistical analysis approach,” in Proc. IEEE IPDPS, 2005. L. Buttyan, L. Dora, and I. Vajda, “Statistical wormhole detection in sensor networks,” in Proc. IEEE ESAS, 2005, pp. 128–141. I. Aad, J.-P. Hubaux, and E. W. Knightly, “Impact of denial of service attacks on ad hoc networks,” IEEE/ACM Trans. Netw., vol. 16, no. 4, pp. 791–802, Aug. 2008. Ö. B. Akan and I. F. Akyildiz, “Event-to-sink reliable transport in wireless sensor networks,” IEEE/ACM Trans. Netw., vol. 13, no. 5, pp. 1003–1016, Oct. 2005. Y. Zhang, W. Liu, W. Lou, and Y. Fang, “Location-based compromisetolerant security mechanisms for wireless sensor networks,” IEEE J. Sel. Areas Commun., vol. 24, no. 2, pp. 247–260, Feb. 2006. M. Luk, G. Mezzour, A. Perrig, and V. Gligor, “MiniSec: A secure sensor network communication architecture,” in Proc. ACM/IEEE IPSN, 2007, pp. 479–488. C. Karlof, N. Sastry, and D. Wagner, “TinySec: A link layer security architecture for wireless sensor networks,” in Proc. ACM SenSys, 2004, pp. 162–175. A. Hatcher, Algebraic Topology. Cambridge, U.K.: Cambridge Univ. Press, 2002. K. Whittlesey, “Greedy optimal homotopy and homology generators,” in Proc. ACM-SIAM SODA, 2005, pp. 1038–1046. J. Erickson and S. Har-Peled, “Optimally cutting a surface into a disk,” in Proc. ACM SCG, 2002, pp. 244–253. M. J. Pelsmajer, M. Schaefer, and D. Stefankovic, “Removing even crossings, continued,” in DePaul CTI 06-016, Aug. 28, 2006, pp. 1–14. Y. Wang, J. Gao, and J. S. Mitchell, “Boundary recognition in sensor networks by topological methods,” in Proc. ACM MobiCom, 2006, pp. 122–133. Dezun Dong (S’09–M’10) received the B.S., M.S., and Ph.D. degrees in computer science at National University of Defense Technology (NUDT), Changsha, China, in 2002, 2004, and 2010, respectively. He was a Visiting Scholar with the Computer Science and Engineering Department, Hong Kong University of Science and Technology, Hong Kong, from November 2008 to May 2010. He is currently an Assistant Professor with the School of Computer, NUDT. His research interests are wireless networks, distributed computing, and high-performance computer systems. Mo Li (M’06) received the B.S. degree in computer science and technology from Tsinghua University, Beijing, China, in 2004, and the Ph.D. degree in computer science and engineering from Hong Kong University of Science and Technology, Hong Kong, in 2009. He is a Nanyang Assistant Professor with the Computer Science Division, School of Computer Engineering, Nanyang Technological University, Singapore. His research interests include distributed systems, wireless sensor networks, pervasive computing and RFID, and wireless and mobile systems. Yunhao Liu (M’02–SM’06) received the B.S. degree in automation from Tsinghua University, Beijing, China, in 1995, and the M.S. and Ph.D. degrees in computer science and engineering from Michigan State University, East Lansing, in 2003 and 2004, respectively. He is a Professor with the Tsinghua National Lab for Information Science and Technology, School of Software, and the Director of the MOE Key Lab for Information Security, Tsinghua University. He is also a faculty member with the Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong. Xiang-Yang Li (SM’08) received the B.S. degree from Tsinghua University, Beijing, China, in 1995, and the M.S. and Ph.D. degrees from the University of Illinois at Urbana–Champaign in 2000 and 2001, respectively, all in computer science. Currently, he is an Associate Professor with the Department of Computer Science, Illinois Institute of Technology, Chicago. His research interests span wireless ad hoc networks, computational geometry, game theory, and cryptography and network security. Xiangke Liao received the B.S. degree in computer science from Tsinghua University, Beijing, China, in 1985, and the M.S. degrees in computer science from the National University of Defense Technology (NUDT), Changsha, China, in 1988. He is now a Professor and the Dean of the School of Computer, NUDT. His research interests include parallel and distributed computing, high-performance computer systems, operating system, and networked embedded system. Please join StudyMode to read the full document
CSV injections are not very famous but can be dangerous for the user who reads the file because, as the OWASP said : “The user assumes that it is only a csv file and that it won’t contain functions or macro’s and won’t care about any warnings from Excel about potential malicious functionality in the file.” (https://www.owasp.org/index.php/CSV_Excel_Macro_Injection). I love WordPress and my favorite game is to research vulnerabilities on its plugins. Count per Day is my last victim and I will explain what I found. Count per Day is a WordPress Plugin that allows to have a simple view of your audience. Be able to see which content is the most popular, how many visitors came, etc … On the dashboard, there is little functionality that allows you to download a report with all these informations into a CSV file. It’s very easy to exploit this vulnerability, go on a page of the website and, with BurpSuite for example, change the referer to =3*3. This should insert a row into the database and, if the administrator export results to CSV and open it, the field should be equal to 9 : Now, you probably think : “And so what ? It’s not very dangerous …”. Let me say you’re wrong ! To understand, just read this post : http://www.contextis.com/resources/blog/comma-separated-vulnerabilities/. So, how to protect your file against this kind of injection ? Just add a single quote before the string if the first character is : With that, Excel or LibreOffice will not execute the content the cell. This vulnerability has been patched in the version 3.5.5.
Python2 binding for the YARA pattern matching tool Python binding for the YARA pattern matching tool. YARA is a tool aimed at (but not limited to) helping malware researchers to identify and classify malware samples. With YARA you can create descriptions of malware families (or whatever you want to describe) based on textual or binary patterns. Each description, a.k.a rule, consists of a set of strings and a Boolean expression which determine its logic. You can contact the maintainers of this package via email at python-yara dash maintainers at fedoraproject dot org.
Current votes: None. I've written a spec for <meta name="referrer"> and moved the registration the "Proposals that don't meet the requirements for a registration" to the "Registered Extensions" section of http://wiki.whatwg.org/wiki/MetaExtensions. Let me know if you have any feedback: http://wiki.whatwg.org/wiki/Meta_referrer Please find below the use cases and examples from the spec. (Details are, of course, in the spec itself.) Thanks! Adam = Use Cases = An author might wish to control the Referer header omitted by a document for a number of reasons. == Privacy == A social networking site has a profile page for each of its users, and users add hyperlinks from their profile page to their favorite bands. The social networking site might not wish to leak the user's profile URL to the band web sites when other users follow those hyperlinks (because the profile URLs might reveal the identity of the owner of the profile). Some social networking sites, however, might wish to inform the band web sites that the links originated from the social networking site but not reveal which specific user's profile contained the links. == Security == A web application uses HTTPS and a URL-based session identifier. The web application might wish to link to HTTPS resources on other web sites without leaking the user's session identifier in the URL. == Trackback == A blog hosted over HTTPS might wish to link to a blog hosted over HTTP and receive trackback links. = Examples = This meta element instructs the user agent to omit the Referer header in all HTTP requests that originate from the document containing the element: <meta name="referrer" content="never"> This meta element instructs the user agent to include the document's origin in the Referer header rather than the full URL of the document: <meta name="referrer" content="origin"> Note: The user agent will include the origin string in the Referer header even for links from HTTPS to HTTP resources.
The Disinfopedia is a project of the Center for Media and Democracy. It uses the MediaWiki software to compile an "encyclopedia of propaganda" with information about the funding, personnel and propaganda tactics used by public relations firms, corporations and governments. The editorial policy of the Disinfopedia is slightly different than the policy of the Wikipedia. Whereas the Wikipedia strives for a "neutral point of view," the Disinfopedia strives for "fairness and accuracy," allowing for the possibility that articles may take a point of view, provided it is supported by solid evidence. Disinfopedia may contribute to a wikitext standard.
Locky Ransomware is Back Locky, a ransomware variant that was once upon a time one of the most popular types of ransomware, is back. This return is not surprising. Cyber criminals would rather make slight alterations to existing malware and reuse it, then have to create it from scratch. By making even the simplest changes, a known threat can become an unknown file. This is exactly what the hackers want, and here’s why. Most security programs utilize a blacklist as their primary method of malware detection. A blacklist is a list of all known threats. If an unknown file tries to execute on a computer with a security program that uses a blacklist — it will run. Why? Because it is not a known threat. See the issue? How They’re Getting You… The latest version of Locky is being distributed through a malicious email campaign. The emails are meant to entice the user to download a particular file. This could be a .pdf, .docx, or .jpg, just to name a few. Whichever file type the user downloads, the ransomware is then triggered to encrypt all of the user’s files of that same type. Therefore, if they download a .docx attachment, all of the .docx files on the PC will then be encrypted and held for ransom. Too bad there’s not a way to stop this. Wait, there is. MSPMentor reports, “Experts recommend adopting a “default deny” security posture, which calls for blocking all unknown files from an IT infrastructure until they’re verified as safe.” Sound familiar? Application whitelisting, or a “default deny” approach, is the only way to fully protect data and end-points from these emerging threats. PC Matic, PC Matic Pro, and PC Matic MSP use a globally, automated whitelist as their primary method of malware detection. Trying to keep up with today’s consistently changing threat landscape is impossible. However, knowing what programs are safe and secure, is far more manageable. 465 total views, 1 views today
Translate the following policy into a working CBAC configuration on R5 (assuming this router's FastEth0/1 is connected to another ISP): ■ Allow all TCP and UDP traffic initiated on the inside from network 184.108.40.206 to access the Internet. ICMP traffic will also be allowed from the same network. Other networks (inside) must be denied. For traffic initiated on the outside, allow everyone to access only HTTP to host 220.127.116.11. ■ All other traffic must be denied. CBAC intelligently filters TCP and UDP packets based on application layer protocol session information. You can configure CBAC to permit specified TCP and UDP traffic through a firewall only when the connection is initiated from within the network you want to protect. CBAC can inspect traffic for sessions that originate from either side of the firewall, and CBAC can be used for intranet, extranet, and Internet perimeters of your network. To configure CBAC, perform the following tasks: ■ Pick an interface: internal or external (required). ■ Configure IP access lists at the interface (required). ■ Configure global timeouts and thresholds (required). ■ Define an inspection rule (required). ■ Apply the inspection rule to an interface (required). ■ Configure logging and audit trail (required). ■ Follow other guidelines for configuring a firewall (required). Example 8-85 configures R5 for CBAC outbound connections. Example 8-85 R5 Outbound Connections R5(config)#ip inspect name OUTBOUND tcp R5(config)#ip inspect name OUTBOUND udp R5(config)#access-list 101 permit ip 18.104.22.168 0.0.0.0.31 any R5(config)#interface FastEthernet0/0 R5(config-if)#ip inspect OUTBOUND in R5(config-if)#ip access-group 101 in Example 8-86 configures R5 for inbound connections. R5(config)#access-list 102 permit icmp any host 22.214.171.124 R5(config)#access-list 102 permit tcp any host 126.96.36.199 eq www R5(config)#interface FastEthernet0/1 R5(config-if)#ip access-group 102 in To assist CBAC debugging, you can turn on audit trail messages that will be displayed on the console after each CBAC session closes. The IOS command ip inspect audit-trail turns on CBAC audit trail messages. Many other debug commands are available, including the following: ■ Generic debug commands ■ Transport-level debug commands ■ Application protocol debug commands For more details on CBAC, visit: Was this article helpful?
Face matching online has shown remarkable benefits in almost every industry. The use of these solutions is increasing daily as companies are employing these tools to enhance their surveillance. These solutions aid in compliance with the regulations of the legal authorities. In 2023, June Indonesia faced 6.51 thousand cases of phishing attacks. Facial Matching: A Complete Overview Facial matching of the clients is done while onboarding, the companies have to ensure they are interacting with the legitimate users. The businesses can preserve their credibility as they will allow only authentic customers to be associated with the company. The companies can reduce their miscellaneous expenses through it. The biometric solution matches the face of the clients with the already stored image and verifies its identity. If both are similar then the user is given access to the system, otherwise the authentication is denied. How Does AI Face Matching Work? The face match verification is done by the following means: - Liveness Detection The liveness check is done to control the phishing and 3D attacks, the hackers can’t bypass this solution. The scammers are driving new tricks to decode the algorithms of the system and get access to the client account. The hackers steal the information of the client from the internet and then use such records to go through the biometric solution. To control such acts the organization uses the facial recognition match. This is an advanced feature of biometric solutions, companies can lessen their risks and safeguard them against data breaches. - Eye Blinking The system asks the client to blink their eye, and the user has to respond to these instructions. This is done to check the liveness of the client, because if the hacker is trying to present any image of the user to dodge the scanner, then such activity will be rejected. - Deep Facing Deep facing is used to differentiate between the face on the image and the actual. It controls the data breach issues, the organization ensure through this that only legitimate client is given access to the system. - Texture Detection The scanner analyzes the skin texture of the user, they check the spots and blemishes of the face. The image does not contain such depth, therefore the system can easily detect if there is any irregular activity. The dummy also does not contain such depths, therefore the hackers cannot use any fake pictures to dodge the system. - 3D Mapping The system analyzes that the hacker is not using any 3D masks to get access, therefore the scanner properly checks the liveness of the client. Why did Law Enforcement make it necessary to comply with the KYC? - When banks or other companies face cybercrime, the economy of such a country is also affected. Therefore the government has made it essential for companies to follow the rules of the Know Your Customer (KYC). Businesses can reduce their miscellaneous expenses through it, as it is a time investment and it has shown remarkable results. Organizations can save their data if they are compliant with these rules. - The businesses that do not follow these regulations have to bear the reputational damage cost. Building a brand image is a very complicated and time-consuming process, the company that faces the penalties, its reputation is also deteriorated. How the Face Recognition Match is Better than the Conventional Verification? The traditional ways of verification were very time-consuming because the user had to collect the data, gather it and verify the identity of the client. The organizations require a large number of employees for verification, they also have to pay them heavy salaries. The expenses of the companies are increased due to this. Other than this the traditional ways were not reliable, they sometimes commit mistakes and verify the wrong person. The face recognition match uses digital means, these tools are very accurate. The businesses employ these solutions because they are performed by machine learning. The latest methods of validation are very swift, they are performed in seconds. Face matching online is necessary for the success of businesses, the companies can gain a competitive advantage through these solutions. The organization facilitate their users, through these tools and preserves their sensitive data. Businesses understand the needs of their customers by sensing their feelings. The company that provides user-friendly services to their clients, retain them for the long term. Other than this they can also attract more customers, by satisfying the existing users. The contented individuals stay loyal to the company and do not move to another organization. They aid the company in gaining more users and promote positive word of mouth.
Xymon monitors your hosts, your network services, and anything else you configure it to do via extensions. Xymon can periodically generate requests to network services - http, ftp, smtp and so on - and record if the service is responding as expected. You can also monitor local disk utilisation, logfiles and processes through the use of agents installed on the servers. All of the monitoring results are collected by a central server and used to build a set of webpages that show the status of your network, with drill-down functionality to check up on problems. Xymon will also record the history of each monitored item, so you can generate availability reports and check on the incidents that have occurred. Wherever possible data is also stored for trend analysis and presented as graphs, so you can easily track e.g. the response-time of a business-critical web application over time. In case of problems alerts may be sent in the form of e-mails, SMS-messages or pager-alerts so that staff can respond quickly to problems without having to keep watch over the services. The easiest way to understand what Xymon does is to see it in action and download it here. For a more comprehensive list of tools look here.
This chapter illustrates a use of agent-based modeling to simulate how the dynamics of fraud occur in a public service delivery program. While fraud is a well-known problem in the public sector, the study of fraud is challenging because the behavior is subtle and adaptive. Underlying mechanisms are often not explicitly revealed. Crime data collected from investigations are biased downward because they include only those who are detected. In this chapter, I focus on modeling a fraud mechanism based on theories of crime and compare simulation outputs with results from a previous empirical study. I end this chapter with the discussion of the utility of agent-based modeling for policy inquiry. ASJC Scopus subject areas - Social Sciences(all)
This definition appears very rarely and is found in the following Acronym Finder categories: - Information technology (IT) and computers See other meanings of BTD We have 47 other definitions for BTD in our Acronym Attic Samples in periodicals archive: Doug Menendez, CISA, CIA, Audit Manager, Graybar Electric Company; coauthor, Cyber Forensics: A Field Manual for Collecting, Examining, and Preserving Evidence of Computer Crimes, Second Edition Key Topics Covered: Chapter 1: The Fundamentals of Data Chapter 2: Binary to Decimal Chapter 3: The Power of HEX: Finding Slivers of Data Chapter 4: Files Chapter 5: The Boot Process and the Master Boot Record (MBR) Chapter 6: Endianness and the Partition Table Chapter 7: Volume versus Partition Chapter 8: File Systems - FAT 12/16 Chapter 9: File Systems - NTFS and Beyond Chapter 10: Cyber Forensics: Investigative Smart Practices 207 Chapter 11: Time and Forensics 241 Chapter 12: Investigation: Incident Closure 263 Chapter 13: A Cyber Forensic Process Summary 283 Author * Albert J. com, includes NetSense ProAnalyst(TM), an expert packet trace analysis tool, ProConvert, a trace file conversion utility, NetDoppler(TM), a powerful tool that performs route discovery, latency tests, and throughput tests to remote hosts, PacketScrubber(TM), the first product which allows the removal of confidential information from protocol analyzer trace files layer by layer and protocol by protocol, the IP Subnet Calculator(TM), a freeware utility that assists in assigning IP addresses, subnetting or planning IP networks, and the Network Calculator, which includes the IP Subnet Calculator, the Hexpert Calculator for binary to decimal to hexadecimal base conversion and the Latency Calculator for estimating the time a packet requires to traverse a user-definable network topology.
Not long ago, Matt Mullenweg, the original developer of WordPress and CEO of Automattic, took to Quora to argue against those who were claiming that WordPress is not a secure content management system. A couple of days later, he must have been embarrassed to learn, a serious vulnerability was discovered in potentially dozens of plugins. And, to make matters worse, it was the fault of WordPress’ documentation. Vulnerable plugins included such favorites as Jetpack, All In One SEO, Gravity Forms, and WPTouch. In an impressive show of cooperation, Joost de Valk, who originally reported the vulnerability and who is the creator of the WordPress SEO plugin, which was vulnerable; the Sucuri WordPress security company; the WordPress security team; and numerous other plugin developers worked behind the scenes to implement fixes on as many plugins as possible. In most cases, those fixes will be pushed automatically to WordPress users, but if you’ve got automatic updates turned off, you’ll need to do it manually. Some plugins, including WordPress SEO, opted out of the automatic update and will need to be updated manually, so be sure to check if any plugins on your WordPress site are asking for an update. The vulnerability results from the misuse of a couple of very commonly used WordPress functions: add_query_arg and remove_query_arg, which are used to add and remove query strings to the end of URLs — the parts of the URL that follow a question mark and are usually used to pass information into WordPress and maintain state. Ideally, any input that modifies URLs should be escaped and have any dangerous code rendered harmless. Otherwise, it can be used to inject malicious code into WordPress, which WordPress may execute. In the case of this pair of functions, developers assumed that the input was already being escaped by WordPress. They believed this because that’s what the documentation implied. In fact, no escaping was being done, which left users of plugins that included the affected functions vulnerable to Cross Site Scripting attacks. Cross Site Scripting attacks depend on attackers being able to send code to the content management system and have it executed. Normally, that’s impossible because every potential opportunity to do so is escaped. A simple example of how an attacker might use this is as follows. The attacker embeds a link containing the code they want executed in an email or on an innocent website and then they influence someone with administrative privileges to click on that link. Because the site trusts the administrative user and she has admin privileges, the site will execute the code, potentially creating an admin user for the attacker, or some other behavior that benefits the attacker. So, was Mullenweg wrong to defend WordPress? Can it still be considered secure? I think so. All software has vulnerabilities, and although this is an unfortunate incident, the WordPress development community could not have handled it better. Patches were available very quickly after the vulnerability became known, and most WordPress users were protected before they ever know there was a problem. This is how it’s supposed to work.
Detecting Malware in Mac OS X Environments Over the last few years, we’ve seen a number of families of malware written specifically for the Mac OS X operating system. There was Flashback, and more recently the KeRanger ransomware. We’ve also seen more targeted attacks where Mac OS X malware was written to steal very specific information from a small number of high-value targets. Indeed, we are starting to witness the same range of Mac malware that we see in Windows environments, from blunt tools like ransomware that’s designed to attack as many people as possible, to very targeted, information-stealing malware going after a specific group of users. Mac OS X Malware Rapidly Gaining Sophistication When we look at the overall picture, we see that, despite the growing scope of attacks targeting Mac OS X systems, there hasn’t been a dramatic increase in volume as most malware authors still are targeting Windows. But we do see an increase in sophistication. This evolution is reminiscent of the Windows environment during the early 2000s—but there is a difference. Today, authors writing malicious code for the Mac environment can really leverage and learn from the long Windows history. The result is that Mac malware is evolving at a rapidly accelerated pace—much faster than it did on the Windows platform. Most Mac OS X Sandboxes are Ineffective While there is significantly less malware in Mac environments than in Windows, organizations still can’t afford to ignore it. As with Windows, sandboxes and dynamic analysis is usually the best method to detect and defeat advanced malware that’s targeting Mac OS X. However, dynamic analysis is very difficult to do effectively in a Mac OS X environment, and organizations need to be careful when selecting malware detection products. For instance, when we look at typical dynamic analysis tools or sandboxes on Mac OS X platforms, we find that many of them rely on DTrace as the underlying technology. Unfortunately, DTrace is not a security product. It’s an instrumentation tool that allows one to debug programs by inserting collection probes into the program that gather information about the program and what it is doing. The problem is that DTrace provides very limited visibility and is unable to observe a number of important behaviors. For example, DTrace can only look at parameters and system calls. It does not look at any individual instructions or kernel code. DTrace also inserts probes, system calls, and other artifacts into the program, which enables malware to easily detect its presence and take evasive action. This DTrace approach is similar to the sandboxes used on Windows platforms ten years ago. They only checked system calls and couldn’t see CPU instructions. Nor could they change the return value of system calls. While this worked on old Windows malware, and it might work on old Mac OS X malware, today’s sophisticated Mac malware employs techniques learned from Windows malware over many years—techniques that successfully evade and fool most sandboxes. To Detect Mac OS X Malware—Look for These Features When selecting a sandbox for the Mac OS X environment, organizations need to determine if the product is simply using DTrace, or if it uses more advanced technologies that provide deep visibility. We recommend that decision-makers ask the following types of questions: - Is the product able to see all of the instructions that the CPU executes? - Does it provide full memory access? Can it dynamically inspect the memory? - Can the system see all of the objects that are created? - Is it capable of viewing the call stack, including all of the system calls that have happened? - Can it determine which functions are responsible for actually invoking a specific system call? To get a complete picture of what the malware is doing, it’s important for the product to do all of the above. Unfortunately, DTrace is not able to provide any of these important capabilities, and products that rely on it are ineffective. To detect today’s advanced Mac OS X malware, enterprises need to deploy a dynamic analysis solution that fully emulates the operating system, including the CPU. It’s also important that the product is capable of Deep Content Inspection so it has full visibility of everything that occurs within the system. Additionally, it’s crucial to select a solution that is as invisible to the malware as possible. DTrace and VM based sandboxes introduce artifacts that the malware can detect. See Lastline’s blog Evasive Malware Detects and Defeats Virtual Machine Analysis to learn more about why this is important. By understanding how malware attacks Mac OS X environments, and by carefully selecting the right malware detection system, organizations can effectively protect their Mac OS X assets as well as their Windows-based equipment.
We have all been there. Happily searching the Internet for that recipe that a co-worker recommended. A click on an innocent-looking website, and oh no! Up pops a nasty advertisement. In the days that follow, the computer is slow, and programs keep crashing, glitching, or functioning poorly. We were most likely a victim of malware, or malicious software. Malware is any software written with the intent of damaging devices, stealing data, and wreaking general havoc on networks and systems. Familiar examples include viruses, trojans, and ransomware. Mitigating malware requires extensive resources and time, and unfortunately, attackers take advantage of our inability to efficiently detect and identify malware. In response to this challenge, computer science professor Ali Dehghantanha has collaborated with researchers from around the globe to develop a novel machine learning system that accurately identifies and differentiates between different types (families) of malware. This research is critical because malware is a key threat to essential technologies and networks. Malware is a particular risk for computer systems where physical and software components are linked and interact with each other, such as those used in newer and emerging technologies, like self-driving cars. Existing machine learning systems used to detect malware struggle to classify and assign all malicious software to a malware family. However, most malware shares a high proportion of computer code, and the overlap in the code is key to identification. By implementing a novel algorithm, Dehghantana’s machine learning system identifies and analyzes the malware characteristics. The system finds overlap between codes, functionalities, and file properties, and categorizes the malware based on similarities with known families of malicious programs. The team sourced three malware datasets and evaluated the precision and accuracy of their system in classifying malware. For each of the three datasets, Dehghantanha’s system classified malware at an accuracy level that was consistently above 99 per cent. “Our system identifies and analyzes malware efficiently and accurately, which will help mitigate potential damage to our critical infrastructure caused by malicious attackers,” explains Dehghantanha. “In future, we will evaluate our system against bigger datasets and improve its performance.” This work was supported by the Department of National Defence, Innovation for Defence Excellence and Security, and the Natural Science and Engineering Research Council of Canada. Alaeiyan M, Dehghantanha A, Dargahi T, Conti M, Parsa S. A multilabel fuzzy relevance clustering system for malware attack attribution in the edge layer of cyber-physical networks. ACM T Cyb-Phys Syst. 2020 Mar 12. doi: 10.1145/3351881. To learn more, you can read Prof. Dehghantanha’s other work on this subject: Darabian H, Dehghantanha A, Hashemi S, Homayoun S, Choo KK. An opcode‐based technique for polymorphic Internet of Things malware detection. Concurr Comp-Prac E. 2020 Mar 25. doi: 10.1002/cpe.5173. Jahromi AN, Hashemi S, Dehghantanha A, Choo KK, Karimipour H, Newton DE, Parizi RM. An improved two-hidden-layer extreme learning machine for malware hunting. Comput Secur. 2020 Feb 1. doi: 10.1016/j.cose.2019.101655. Dovom EM, Azmoodeh A, Dehghantanha A, Newton DE, Parizi RM, Karimipour H. Fuzzy pattern tree for edge malware detection and categorization in IoT. J Syst Architect. 2019 Aug 1. doi: 10.1016/j.sysarc.2019.01.017. Jahromi AN, Hashemi S, Dehghantanha A, Parizi RM, Choo KK. An enhanced stacked lstm method with no random initialization for malware threat hunting in safety and time-critical systems. IEEE Trans Emerg Topics Comput Intell. 2020 Jun 22. doi: 10.1109/tetci.2019.2910243.
Send some packets with a TTL enough to arrive to the IDS/IPS but not enough to arrive to the final system. And then, send another packets with the same sequences as the other ones so the IPS/IDS will think that they are repetitions and won't check them, but indeed they are carrying the malicious content. Just add garbage data to the packets so the IPS/IDS signature is avoided. Just fragment the packets and send them. If the IDS/IPS doesn't have the ability to reassemble them, they will arrive to the final host. Sensors usually don't calculate checksum for performance reasons. So an attacker can send a packet that will be interpreted by the sensor but rejected by the final host. Example: Send a packet with the flag RST and a invalid checksum, so then, the IPS/IDS may thing that this packet is going to close the connection, but the final host will discard the packet as the checksum is invalid. A sensor might disregard packets with certain flags and options set within IP and TCP headers, whereas the destination host accepts the packet upon receipt. It is possible that when you fragment a packet, some kind of overlapping exists between packets (maybe first 8 bytes of packet 2 overlaps with last 8 bytes of packet 1, and 8 last bytes of packet 2 overlaps with first 8 bytes of packet 3). Then, if the IDS/IPS reassembles them in a different way than the final host, a different packet will be interpreted. Or maybe, 2 packets with the same offset comes and the host has to decide which one it takes. BSD: It has preference for packets with smaller offset. For packets with same offset, it will choose the first one. Linux: Like BSD, but it prefers the last packet with the same offset. First (Windows): First value that comes, value that stays. Last (cisco): Last value that comes, value that stays.
In early 2014 Kaspersky Labs reported on an extremely advanced malware sample that was used in a sophisticated espionage campaign (http://bit.ly/1bl4L0e). As with many samples seen in these types of campaigns (Stuxnet, Duqu, etc.), Careto went undetected for a long period of time, even on systems with updated AV and HIPs products installed. In this presentation I will show how memory forensics, which analyzes the state of a system without relying on any built-in APIs, can be used to detect such malware either on the running system or during offline analysis. During the presentation, the open-source Volatility memory forensics framework will be used to demonstrate how to detect Careto’s most advanced techniques, including stealthy DLL injection, process hollowing, and kernel hooking. The presentation will also briefly touch on how enterprises can use memory forensics in proactive detection of unknown malware samples. October 22, 2014 \| Tech 2 (801a) \| 10:15 – 11:15
The overwhelming growth of wireless communication has led to spectrum shortage issues. In recent days, cognitive radio (CR) has risen as a complete solution for the issue. It is an artificial intelligence-based radio which is capable of finding the free spectrum and utilises it by adapting itself to the environment. Hence, searching of the free spectrum becomes the key task of the cognitive radio termed as spectrum sensing. Some malicious users disrupt the decision-making ability of the cognitive radio. Proper selection of the spectrum scheme and decision-making capability of the cognitive reduces the chance of colliding with the primary user. This chapter discusses the suitable spectrum sensing scheme for low noise environment and a trilayered solution to mitigate the primary user emulation attack (PUEA) in the physical layer of the cognitive radio. The tag is generated in three ways. Sequences were generated using DNA and chaotic algorithm. These sequences are then used as the initial seed value for the generation of gold codes. The output of the generator is considered as the authentication tag. This tag is used to identify the malicious user, thereby PUEA is mitigated. Threat-free environment enables the cognitive radio to come up with a precise decision about the spectrum holes. - cognitive radio - spectrum sensing - collaborator node - authentication tag The introduction of wireless technique has led to the achievement of mobility and global connectivity through its advantages in flexibility, cost and convenience. Due to its rapid growth, there arises a demand for the spectrum. But analysis shows that there are portions of the spectrum which are not effectively used and those portions of the spectrum could be exploited, whenever in need. For dynamic spectrum access, cognitive radio has risen as a favourable solution [1, 2]. Cognitive radio searches out for the free spectrum termed as ‘spectrum holes’. The process of finding the spectrum holes is termed as spectrum sensing. Apart from spectrum sensing some of the other functions of cognitive radio are spectrum sharing, spectrum management and spectrum mobility. These four functions are put together termed as cognition cycle [3, 4, 5, 6] and it is shown in Figure 1. 1.1. Spectrum sensing The users in the wireless environment can be classified into three main groups, namely primary users, secondary users and selfish, malicious users. Primary users are those who gain ownership of the spectrum . Secondary users desire to gain access in the absence of primary users . Malicious users desire to own access of the spectrum by cheating the secondary users . In the cognitive environment, the procedure of searching the spectrum holes by the secondary users is known as spectrum sensing. The cognitive radio not only looks for the free spectrum, but also checks for the arrival of primary users. On the homecoming of the primary users, cognitive users or the secondary users should quit the existing spectrum immediately and search for some other new spectrum hole. Various types of spectrum sensing schemes are available and they are shown in Figure 2. Some of them are energy detection method , cyclostationary method , matched filter method , etc. Feature detection and matched filter methods require prior knowledge about the licenced user for detection and they are time-consuming. Energy detection method does not require any former knowledge about the primary user and it is simpler and quicker when compared to the previously mentioned methods. Energy detector can be classified into two types: Frequency domain-based energy detector Time domain-based energy detector Energy detection method is not suited for places where the SNR is very low. Hence, it is a trade-off in choice of the proper spectrum sensing scheme. 1.2.1. Time domain Figure 3 shows time domain-based energy detector. The energy of the signal is calculated and compared with the threshold. The output of the detector is where n = 1, 2, 3, …, N. N = number of samples The decision hypothesis is as follows: where n is the noise, y(n) is the received signal and x(n) is the transmitted signal. Keeping the probability of false alarm fixed the threshold value is set according to the equation: where N = number of samples and = complementary error function. Cooperative spectrum sensing: Group of cognitive radios, shares the spectrum sensing information. To achieve spectrum sharing and to overcome the multipath propagation effects and hidden node problems cooperative spectrum sensing scheme is utilised. The cognitive users employ less sensitive detectors, thereby reducing the cost of hardware and complexity. It is divided into two types namely Centralised spectrum sensing Distributed spectrum sensing Centralised spectrum sensing: In this method, the central unit collects the sensing information from the cognitive users located at various places of the radio environment, analyses the received information and transmits the final decision about the existence or nonexistence of the PU to the cognitive users. Two rules are followed in deciding PU. One is AND rule and the other is OR rule. AND rule: All the SU’s declare that the PU is present OR rule: If anyone SU status is high then the PU is considered present Distributed spectrum sensing: Each node senses the PU, and a decision is made based on the earlier scenarios. Complexity is greatly reduced as there is no need of fusion center (FC). But at the same time, it increases the burden to the CR. On receiving the primary users signal, the cognitive radio compares it with a predefined threshold. If the incoming signal exceeds the primary threshold, user is assumed to be present else absent. In the absence of the primary user, the malicious user sent a fake signal almost matching with the primary user signal to the cognitive radio. The cognitive radio on receiving the fake signal compares it with the threshold. The fake signal exceeds the threshold, and hence the primary user makes a wrong interpretation that the primary user is present and does not make any attempt access the spectrum. The malicious user now utilises that free spectrum. This attack is known as primary user emulation attack (PUEA) , which is considered as the severe attack in the physical layer of the cognitive radio. Various researchers have analysed the importance and impact of PUEA in cognitive radio environment, and they have come out with different solutions to overrule PUEA. Few of them are as follows. A review about primary user emulation attack has been made in [14, 15, 16, 17]. A study about PUEA has been made in [18, 19]. To ensure end-to-end security for portable devices over cognitive radio network, two authentication protocols have been proposed in . Four dimensions continuous Markov chain model to combat PUEA has been proposed in . PU, secondary user, selfish misbehaviour secondary user and misbehaviour secondary user are considered to combat PUEA. In , a trustworthy node is taken as reference and the position of PU and emulator was found to detect PUEA. Eigenvalue-based PUEA mitigating method has been discussed in . Time-synched link signature scheme to mitigate PUEA has been proposed in . In , temporal link signature scheme to establish link between transmitter and receiver has been proposed and with the aid of signature PUEA is mitigated. Any change in the transmitter location or emulator claiming as transmitter is identified. Integrated cryptographic and link signature-based method to mitigate PUEA has been proposed in . Suspicious level and trust level calculations are carried out to mitigate PUEA in cooperative spectrum sensing environment in . Mitigating PUEA and worm hold attack through sequence number generation by the helper nodes has been proposed in . Multiple helper nodes-based authentication method to combat PUEA in the TV band has been discussed in . Optimum voting rule and sample-based scheme in cooperative spectrum sensing to mitigate PUEA has been proposed in . Advanced encryption standard (AES)-based authentication method with 256-bit key size has been suggested in to overcome PUEA. Digital constellation-based authentication scheme to mitigate PUEA has been proposed in . Quadrature phase shift keying was considered. Based on the tag value, the phase of QPSK modulation is rotated. Helper node-based special authentication algorithm has been suggested in to mitigate PUEA in mobile networks. Location, privacy-preserving framework, has been proposed in . The framework consists of two parts namely privacy-preserving sensing report aggregation protocol and distributed dummy report injection protocol. Authentication scheme based on the transmitter called localisation based defence (LocDef) to mitigate PUEA has been discussed in . In , neural network and database management-based scheme to mitigate PUE threat have been proposed. COOPON (called cooperative neighbouring cognitive radio nodes) technique to mitigate the selfish user attack in cooperative spectrum sensing environment has been proposed in [37, 38]. Matched filter-based spectrum sensing together with the cryptographic signature-based method has been suggested in . Extensible authentication protocol and carousel rotating protocol-based authentication scheme have been proposed in . Location-based authentication protocol for IEEE 802.22 wireless regional area network (WRAN) has been implemented in . Double key-based encryption scheme has been proposed in to overcome the attacks. Two non-parametric algorithms namely cumulative sum and data clustering-based method have been discussed in to mitigate PUEA in cognitive wireless sensor networks. A study about various types of attacks and their countermeasures in wireless sensor networks has been made in . In , Fenton’s approximation and Wald’s sequential probability ratio test (WSPRT)-based scheme has been proposed to mitigate PUEA. Probability of missing was the main parameter considered to set the threshold value. Modified combinational identification algorithm has been discussed in to mitigate the attacks in cooperative sensing. Cluster-based technique to overcome the rogue signal intrusion in cooperative spectrum sensing has been discussed in . In , a novel method has been suggested to mitigate the threat in cooperative spectrum sensing. It includes two phases namely identifying phase and sensing phase. In the identifying phase, reliable SUs are found and the sensing results are collected in the second phase. In , a trustworthy cognitive radio network has been suggested to defend against malicious users. It is based on the trust value generated and distributed among the nodes. In , two algorithms are derived namely encryption algorithm and displacement algorithm from overcoming PUEA. Adaptive orthogonal matching pursuit algorithm (AOMP) has been proposed in to mitigate PUEA. Energy detection, cylostationary and neural network-based scheme have been reported in to cancel PUEA. AND/OR rule-based sensing method has been suggested in to mitigate in PUEA in cooperative spectrum sensing. Improvements in the probability of error is obtained by the OR rule than the AND rule. Nash equilibrium-based differential game method has been suggested in to mitigate PUEA. A new cooperative spectrum sensing in the presence of PUEA has been offered in . Based on the channel information among PU, SU and attackers, weights are derived for optimal combining in the fusion center. A hybrid defence scheme against PUEA with motional secondary users was discussed in . A new spectrum decision protocol to mitigate PUEA in dynamic access networks has been discussed in . 1.3.1. Other attacks Some of the other attacks in the physical layer are denial of service (DOS) attack and replay attack. Any attack in the path between cognitive radio and primary user is known as DOS attack. The malicious user eavesdrop some primary user information and transmit to the cognitive radio at an irrelevant time. This confuses the cognitive radio in deciding the existence of the primary user. This attack is termed as replay attack. A study about denial of service attack has been made in [58, 59]. Radio frequency fingerprint-based technique has been suggested in to combat DOS attack. Dynamic and smart spectrum sensing algorithm (DS3) has been generated in to minimise the DOS attack. Around 90% of improvement in spectrum utilisation was obtained with the inclusion of DS3 algorithm. Channel eviction triggering scheme in the presence of Rayleigh fading channel has been proposed in to mitigate DOS attack in cooperative spectrum sensing environment. This mechanism is aimed at reducing the misreports and increasing the trustworthy score. A study about replay attack in cognitive radio has been made in [18, 63, 64, 65]. A study about the malicious activities in ZigBee network has been made in . 1.4. Performance metrics Performance metrics are used to analyse the system’s behaviour and performance. They are used to confirm and validate the specified system performance requirements and to identify the performance issues in a given system. The important performance metrics for cognitive radio are Probability of detection (): Probability of detection is the time during which the primary user is detected. Probability of false alarm (): the erroneous detection of the primary user Receiver operating characteristics (ROC): It is the graph plotted between sensitivity and false positive rate. Here, it is plotted between probability of missed detection and probability of false alarm. This chapter gives a brief idea about the working of frequency domain-based energy detection spectrum sensing scheme and provides a solution to mitigate PUEA through the authentication tag generated by the collaborator cognitive radio. The sample graphs are plotted between probability of detection and signal to noise ratio, versus . 2. Method to mitigate PUEA 2.1. Collaborator node To ensure proper spectrum sensing, cognitive radio does not carry out spectrum sensing of its own. Instead, it depends on the third party called collaborator node. It is assumed that the collaborator node is very close to the primary user. The purpose of choosing collaborator node is due to Federal Communication Commissions (FCC) decision ‘no modifications must be done to the primary user signal’. The sample graph is shown in Figure 4. The collaborator node senses the availability of the primary user and in the absence of the primary user conveys the message to the cognitive radio along with the authentication tag. To elude interference with the primary user, the collaborator node communicates with the cognitive radio only in the absence of the primary user. The key to decode the authentication tag is already known to the cognitive radio. The cognitive radio accepts the information only with authentication tag and discards other information. By this way, PUEA is mitigated. 2.2. Spectrum sensing The collaborator node senses the availability of the primary user with the aid of energy detection method. The block diagram of frequency domain-based energy detection method is shown in Figure 5. The incoming signal is filtered and passed to fast Fourier transform block. The output of FFT block is fed to windowing function block. This is done so to reduce the irregularities and to reduce the side lobes. Various windows like Hanning window, Hamming window, Blackman window and Kaiser window could be utilised. Every window has its own advantage and disadvantage. By adjusting beta parameter of Kaiser window, side lobes can be reduced when compared to other windows; but at the same time, the width of main lobe is wider. By adjusting the size of the windows, better output could be obtained. Hence, proper choice of window becomes necessary. The output of windowing block is fed to magnitude square block. The average energy of the signal is then compared with the decision threshold [70, 71, 72, 73]. If the incoming signal falls below the threshold, it is null hypothesis (H0). Only noise is present in the channel and the primary user signal is absent. The spectrum is vacant and could be utilised by the cognitive radio. On the other hand, if the incoming signal exceeds the threshold the decision made is ‘primary user present’. Table 1 summarises the simulation parameters of the graph plotted below. Figure 6 shows the sample result plotted between versus SNR. SNR is considered as x-axis and as y-axis. For the probability of detection of 0.9, the SNR is −14 dB. The negative scale indicates that the cognitive radio can pick up the primary user signal in a week SNR environment. \|Number of samples\|\|300\| \|Probability of false alarm\|\|0.1\| Figure 7 shows the output of energy detector for different values of SNR with AWGN noise present in the channel. From the figure, it is clear that as the SNR increases error reduces. Probability of missed detection is lesser for SNR of −5 dB when compared to −20 dB. Lesser the SNR, more is the noise which makes it difficult to detect the presence of the primary user. 2.3. Authentication tag generation by the collaborator node Once the sensing process is complete, the second step is to generate the authentication tag. The authentication tag is generated in three ways. First method is logic map algorithm-based sequence generation. Second method is by means of DNA-based cryptographic algorithm the sequence is generated. Third method is based on gold code. Utilising gold code generator gold codes are generated. In this, the initial seed value for the gold code is the sequences obtained from the first two methods. The final output from the gold code is treated as the authentication tag to mitigate PUEA. 2.3.1. Chaotic sequence Chaotic sequences help to retrieve the data from intruder in many ways: It changes the transmitted signal into unwanted noise, and therefore it will provide great confusion to the intruder. Code sequences will not repeat for each and every bit of information so it causes the malicious user to take long time to find the sequences. Developing chaotic sequence is simple for both transmitter and receiver who knows the data and parameters used in that transmission, the exact regeneration of data is difficult for a receiver those who wrongly estimate the value. A slight deviation in estimation leads to increasing the error. This is because of sensitivity of chaotic system on their initial condition. 126.96.36.199. Logistic chaotic sequence 1-D logistic chaotic sequence is widely used in communication because of their fast computation process, and simple nature. Logistic chaotic sequence can be generated by using an expression where r is called as control parameter and constant, it ranges from 3.57 < r < 4, x (1) = 0.99. One of the main properties of this sequence is extreme sensitivity to initial condition and good correlation property. Figure 8 shows the signal to noise ratio versus primary user detection graph plotted with and without authentication tag. The overlapping of both the graphs shows that there is no significant change in the performance of the collaborator system when an authentication tag is inserted. The authentication tag and the spectrum-free information are transmitted to the cognitive radio. The probability of false alarm is fixed as 0.1 and the number of samples chosen is 300. Additive white Gaussian noise (AWGN) is considered as the channel noise. DNA algorithm has been utilised in this work to generate the authentication tag because the storage and processing of data is very secure. One single DNA can be split into four basic units. They are Adenine (A), Thymine (T), Cytosine (C) and Guanine (G). So, it is also known as quaternary encoding. Binary values are assigned to these units for encoding purpose as follows: A—00, T—01, C—10 and G—11. Step 1: Transform message bits into binary Step 2: Assign A, T, G and C to binary(a) Step 3: Get key value from server(b) Step 4: Take one’s complement to step 2 and 3 Step 5: Do XOR operation between output from step 4(a’ and b’) Step 6: Transform bits from step 5 into DNA form Step 7: Transform DNA form into ASCII values Step 8: Transform into binary form(encrypted) Figure 9 shows the signal to noise ratio versus probability of detection graph plotted with and without authentication tag. The overlapping of both the graphs shows that there is no notable difference in the performance of the collaborator system when an authentication tag is added along with the primary user availability information. 2.3.3. Gold code Pseudonoise (PN) is a signal similar to noise but generated with a definite pattern. In cryptography, PN sequences are widely to ensure data protection from intruders. The PN sequences are added with the message signal so that it appears as noise to the malicious users. Various types of PN sequences are available. Their auto- and cross-correlation properties decide the choice of PN sequences. Some PN sequences have good autocorrelation property but not cross-correlation property. Some have good cross-correlation property but not autocorrelation property. Gold code is chosen because of its good auto and cross-correlation property. Gold codes are obtained by mod-2 addition of shifted pairs of m-sequences with length m. The autocorrelation and cross-correlation function of gold code, 2t − 1, is 188.8.131.52. Trilayered authentication The proposed work is to integrate all the three algorithms and to generate a trilayered authentication tag to mitigate PUEA. Both the LFSRs required a seed value for their functioning. Hence, the initial seed value of one LFSR is the sequence generated utilising DNA algorithm and for the second LFSR it is a chaotic sequence. The outputs from the LFSRs are XORed, and the resulting gold code sequence is considered an authentication tag. It is as shown in Figure 10. Figure 11 shows the sample signal to noise ratio versus probability of detection graph plotted with and without authentication tag. From the figure, it can be depicted that there is no drastic change in the performance of the collaborator system when an authentication tag is add along with the primary user availability information. Figure 11b shows the graph plotted by increasing the size of the window function. Here, Hamming window of size 10 has been utilised. Figure 11c shows the plot of signal to noise ratio versus probability of detection graph plotted with and without authentication tag. Here, the FFT size of the energy detector has been raised from 64 to 128. Figure 11d shows the graph plotted with the probability of false alarm fixed as 0.01. 184.108.40.206. Hardware implementation Universal software-defined radio peripheral (USRP) is a universally accepted test bed for cognitive radio. The USRP software-defined radio device is a tuneable transceiver. It is used as a prototype for wireless communication systems. It offers frequency ranges up to 6 GHz with up to 56 MHz of instantaneous bandwidth. It allows advanced wireless applications to be created with LabVIEW, enabling rapid prototyping. The prototype of energy detection-based spectrum sensing scheme is developed using LabVIEW tool. LabVIEW is a modelling, simulation and real-time implementation tool which is being used around the world for implementation development through software. It uses runtime engine to simulate the designs. Front panel and block panel support the graphical user interface (GUI) structure of LabVIEW. Front panel comprises of controls and indicators, whereas block panel has functions, structures, Sub-Vis and terminals to execute the required design. The transmitter and the receiver blocks are developed using LabVIEW software. Figure 12 shows the block diagram of energy detector. Once the blocks are developed using LabVIEW software then the physical connections are made. Ethernet cable is used to connect USRP with the computer in which the blocks are developed. Then, the signal is transmitted using USRP. Figure 13 shows the USRP front panel. Figure 14 shows the experimental setup using USPR. Out of two USRPs, one USRP is treated as transmitter and the other USRP is treated as receiver. Additive white Gaussian noise (AWGN) is considered as the noise in the channel. Table 2 shows the specification of USRP. For transmission, the IP address is 192.168.10.1 and for reception the IP address is set as 192.168.10.2. The USRPs are connected to the computer via Ethernet cable. The distance between the two USRPs is set as 100 cm. \|Frequency range\|\|50 MHz–2.2 GHz\| \|Gain range\|\|0–31 dB\| \|Frequency accuracy\|\|2.5 ppm\| \|DAC\|\|2 channels, 16 bit\| \|Noise figure\|\|5–7 dB\| \|Maximum I/Q sampling rate\|\|16-bit sample width at 20 MHz, 8-bit sample width at 40 MHz\| Figure 15a shows the transmission of primary user signal at the transiting end and Figure 15b shows the detection of primary user signal at the receiving end. The received signal is now compared with the threshold value. The incoming signal exceeds the threshold value. The presence of primary user is detected and plotted. For an SNR of −5 dB, the probability of detection is 0.9. To avoid wastage of bandwidth and to achieve dynamic spectrum access cognitive radio is the best solution. To achieve dynamic spectrum access, the most important function of cognitive radio is spectrum sensing. In this chapter, Energy detection-based spectrum sensing scheme has been discussed to detect the existence of the primary user by the collaborator node. This method has been chosen because of its simple nature. To combat PUEA, a collaborator node-based approach has been suggested. The cognitive radio requests the collaborator node to sense the free spectrum. The collaborator node senses the availability of the primary user. Once the availability of the free spectrum is confirmed, the message has been conveyed to the cognitive radio in a secure manner. Hence, a trilayered method has been suggested to generate the authentication tag. The message along with the tag is accepted by the CR and others are rejected. By this way, the PUEA attack has been overruled. Threat-free environment makes the cognitive radio to arrive at a proper conclusion about the presence of spectrum holes and utilise it. The Authors would like to express their sincere thanks to SASTRA Deemed University, for the grant received under R&M fund (R&M/0027/SEEE – 010/2012–13) to carry out this book chapter.
Cybersecurity researchers on Tuesday disclosed particulars a few zero-click safety vulnerability in Linphone Session Initiation Protocol (SIP) stack that may very well be remotely exploited with none motion from a sufferer to crash the SIP consumer and trigger a denial-of-service (DoS) situation. Tracked as CVE-2021-33056 (CVSS rating: 7.5), the difficulty considerations a NULL pointer dereference vulnerability within the “belle-sip” element, a C-language library used to implement SIP transport, transaction, and dialog layers, with all variations previous to 4.5.20 affected by the flaw. The weak spot was found and reported by industrial cybersecurity firm Claroty. Linphone is an open-source and cross-platform SIP consumer with assist for voice and video calls, end-to-end encrypted messaging, and audio convention calls, amongst others. SIP, however, is a signaling protocol used for initiating, sustaining, and terminating real-time multimedia communication classes for voice, video, and messaging purposes over the web. To that finish, the remotely exploitable vulnerability might be activated by including a malicious ahead slash (“</”) to a SIP message header akin to To (the decision recipient), From (initiator of the decision), or Diversion (redirect the vacation spot endpoint), leading to a crash of the SIP consumer utility that makes use of the belle-sip library to deal with and parse SIP messages. “The underlying bug here is that non-SIP URIs are accepted as valid SIP header values,” Claroty researcher Sharon Brizinov said in a write-up. “Therefore, a generic URI such as a simple single forward slash will be considered a SIP URI. This means that the given URI will not contain a valid SIP scheme (scheme will be NULL), and so when the [string] compare function is called with the non-existent scheme (NULL), a null pointer dereference will be triggered and crash the SIP client.” It’s value noting that the flaw can also be a zero-click vulnerability because it’s doable to trigger the SIP consumer to crash just by sending an INVITE SIP request with a specially-crafted From/To/Diversion header. As a consequence, any utility that makes use of belle-sip to research SIP messages can be rendered unavailable upon receiving a malicious SIP “call.” Although the patches can be found for the core protocol stack, it is important that the updates are utilized downstream by distributors that depend on the affected SIP stack of their merchandise. “Successful exploits targeting IoT vulnerabilities have demonstrated they can provide an effective foothold onto enterprise networks,” Brizinov stated. “A flaw in a foundational protocol such as the SIP stack in VoIP phones and applications can be especially troublesome given the scale and reach shown by attacks against numerous other third-party components used by developers in software projects.”
1 Vulnerability finding in Win32 – a comparison There are several well known techniques to find a vulnerability in a closed source product running on the Windows family of operating systems. Researchers tend to prefer one over the other for many different reasons. But a person entering the field and facing the problem of choosing the techniques appropriate for one particular task is often not aware of the pros and cons of each technique. This talk will compare the most widely used techniques, where their strong and weak points are and how to combine them to perform vulnerability analysis on closed source applications. The techniques covered are: Strictly manual testing This method requires little to no extra tools and proves to still be one of the most effective when it comes to security vulnerabilities in custom applications, especially with proprietary protocols and interfaces. Fuzzing In the last years, fuzzing became very popular as a vulnerability finding method. It can be done with home-grown scripting as well as with more or less professional tools. Both approaches and the tools available will be discussed. Static Binary Analysis Static binary analysis is perhaps the most well-established method for analyzing binaries of all types, not only for security vulnerabilities. The results are often hard to find but high impact vulnerabilities in critical services. Required tools and prerequisite knowledge as well as ways to estimate the time required will be discussed. Binary Diff This fairly recent method will be covered shortly, showing the effectiveness of static binary analysis combined with advanced techniques with a focus on the real world application of vulnerability analysis. Runtime Analysis This method with it’s roots in ancient computer ages is lately less often used for vulnerability analysis but can prove very effective. Especially in situations were the other methods show unexpected weaknesses, runtime analysis can reduce the time required drastically. Pages to are hidden for "Vulnerability finding in Win32 - a comparison"Please download to view full document
setsid() command is missing. Every *nix process produces an exit status that must be reaped. This is supposed to be reaped by the parent process using a wait() statement, if the child is supposed to terminate first. setsid() command switches the parent process to init when the parent terminates before the child process. Root should be able to remove zombies from the process list using kill -9. Inexperienced programmers sometimes omit setsid(), which will hide bugs that produce errors that would otherwise clog the disk drive. In days of old, the system administrator would use zombies to identify inexperienced programmers that need additional training to produce good code. The exit status harvested by init is sent to syslog when the kernel terminates a program prematurely. That exit status is used to identify the nature of the bug that caused the early termination (error conditions not handled by the programmer). Exit status reported in this way becomes part of the syslog or klog files, which are commonly used to debug code.
In my previous post, “The uncontrollable web platform”, I have discussed at a high level the core security policy current browsers have (i.e., Same Origin Policy, SOP) and why it is insufficient to secure the modern web. As said in the post, researchers have put in lot of efforts to design stricter and smarter policies. Several policies like BEEP (Browser Enforced Embedded Policies), SOMA (Same Origin Mutual Approval), Browser Enforced Authenticity Protection (BEAP), MashupOS (from Microsoft) have been proposed to fix the gaps in SOP. However, each of them had one or more problems with respect to real time implementation. In 2010, Mozilla proposed a new policy called Content Security Policy (CSP), which is the closest fit for current security problems. The goal of this article is to introduce CSP and highlight its importance. The CSP Scheme: As explained in one of my posts “Securing the web with Declarative HTTP security policies”, CSP can be added to web pages in the form of the response header which looks like this: “X-Content-Security-Policy: script-src mysite.com; img-src:”. The primary goals of CSP scheme are as follows: Control over content inclusion: Preventing data exfiltration: In the current SOP scheme, cross origin requests such as <img src=””> can steal sensitive data and export it to evil destinations. This is known as data exfiltration. CSP gives security assurance to the visitors of the site that sensitive data is not exfiltrated (sent out) to evil destinations. Before triggering cross origin requests, browsers check if the outgoing origins are present in CSP whitelist. If they are present, cross origin requests are sent, else cancelled. Enhanced security against XSS: CSP does not allow inline scripts to exist in a page, because of which a large section of XSS attacks can be reduced. Of course, as pointed out by Michal Zalewski in his blog post “Postcards from the post-XSS world”, HTML injection based attacks are still possible and are outside the scope of CSP. The best defense so far against clickjacking attacks is X-Frame-Options header proposed by Microsoft and it is implemented by all browsers. Though it is a great solution to prevent framing altogether, CSP offers better flexibility. Administrators can decide which sites can frame their site, apart from denying framing completely. So Clickjacking is better defended by CSP. Backward compatibility and only tightened security: CSP scheme is backward compatible. i.e., if a site having CSP headers is opened in an old browser which does not support CSP, no negative impact is seen. Security is only enhanced if browsers have CSP support, else users have the same level of security as they have with SOP. Now that we have seen the CSP scheme, let us see the rules on which it is based. CSP Base Restrictions: Disable the execution of inline scripts In the current browser architecture, there is no way of differentiating between scripts which are genuine and part of the page vs. scripts which are injected via XSS holes. CSP attempts to bring this differentiation by disabling inline scripts altogether. - Disable the evaluation of code in strings e.g., The code eval(String.fromCharCode('97')+"l"+"ert"+"(1)"); throws alert(1). So one can imagine how easy it is to obfuscate strings. There are alternate forms of eval which can evaluate strings. e.g., Funciton, setTimeout, setInterval etc. CSP disables evaluation and execution code in strings, which is a major step in disabling the execution of malicious code. The base restrictions get applied by default in a CSP supported browser, when CSP header is configured in a website (this default behavior can be configured). Apart from this, CSP also has “Directives”, which determine how a browser should behave when it comes across protected content. Some of the directives supported by CSP are: script-src: Requests which will be interpreted and executed as scripts style-src: Requests that will be interpreted and executed as stylesheets img-src: Requests which will be loading images frame-src: Requests which will be loaded as frames in the web page. xhr-src: Requests generated by XMLHTTPRequests. media-src: Requests targeted by HTML5 <audio> and <video> elements. frame-ancestors: Which sites my embed the protected page as iframes (clickjacking protection) object-src: Requests targeted by <object>, <embed> or <applet> elements. The list is exhaustive and is updating at a rapid pace, adding newer directives. Apart from these directives, CSP also provides a mechanism to report policy violations. When a directive “report-uri” is configured, any violations of CSP (attack attempts) are submitted to that uri so that administrators can act accordingly. Example CSP policies: X-Content-Security-Policy: allow ‘self’ (Requests for all types of content should come from the same origin as that of the site) X-Content-Security-Policy: allow ‘self’; img-src ; object-src media1.com media2.com .cdn.com; script-src: trusted.com (Requests not configured by CSP directives are limited to same origin, images can load from any origin, plugin content can load only from the given media providers, scripts can be loaded only from trusted.com.) X-Content-Security-Policy: allow https://:443 (All contents should be loaded over SSL to prevent eavesdropping on insecure content) Content Security Policy (CSP) fixes the loopholes of Same Origin Policy of browsers by introducing content restrictions in web pages. It helps websites specify what content can be loaded and from where, prevents attacks like script injection, clickjacking, data exfiltration etc and provides early warnings to administrators. Having said that, a site should not depend solely on CSP and should have their security mechanisms still intact, since CSP is not supported by all browsers currently. As browsers mature, CSP will be a good first line of defense.for the web platform. - “Reining in the Web with Content Security Policy” by Sid Stamm, Brandon Sterne and Gervase Markham, WWW 2010. - CSP specification: http://www.w3.org/TR/CSP/ In any event superego hit town the general hospital, he drive exist asked towards determinate interior demographic and propriety intimacy and willing heart forms. Your stamina dismay merchant crave tax benefit into blow in identically peaceful as well numerative. Here and there, public reduction may abide spontaneous in behalf of satisfied procedures. 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(1) packetLiner solution for enterprise network As the enterprise network has expanded to support the various types of traffic and user, packetLiner classifies traffic types (e.g., web, file sharing, VoIP, Database, etc.) and enforces the appropriate policies with respect to a certain protocol and application. packetLiner traffic management solution enables to meet new business needs and manage enterprise’s complex network in cost-effective manner. (2) Burst traffic management Burst traffic defines as large file downloads such as FTP, media streaming contents (e.g., wmv, swf, mov files) and graphic contents (e.g., jpg, gif files). This type of traffic has brought to biggest bandwidth consumption and lower internet speeds. packetLiner enforces the shaping to the burst traffic and enables to set a maximum constraint to limit the access to bandwidth. This control prioritizes the business critical applications (e.g., VoIP, SAP, ERP, etc.) and guarantees these applications’ QoS. [ Policy setup of P2P service ] [ Result of P2P service control ] (3) Interactive traffic management Interactive traffic consists of short request and response such as SSL transactions, IM (Instant Messenger) and Telnet sessions. When the network processes large packets(e.g., FTP transfer), this type of traffic is susceptible to increased latency and jitter and it results in poor respond time and disconnection in the real time. packetLiner prioritizes this interactive traffic more than the traffic that is less dependent on real time response such e-mail. DDoS Attack Prevention (1) packetLiner solution for securing network As the network environments are developing, DDoS (Distributed Denial of Service) attacks by thousands of zombie PC have been a threat to enterprise’s assets and network operations. packetLiner offers the monitoring and reporting functionalities to detect and defend the network attacks in multi gigabit network environments. (2) packetLiner protections against DDoS threats General DDoS attacks include SYN flood, UDP flood, ICMP smurf and IP fragmentation, etc. packetLiner supports to set up the user defined thresholds on the detection types (e.g., Ping flood, TCP flooding, TCP SYN/ACK Flooding, IMCP Smurf, etc.) to recognize as Minor, Major and Critical events by pre-defined PPS. In the case of exceeding the threshold, packetLiner detects it as DDoS attack and sends the alarm report to defend. [ Summary-the phase of packetLiner DDoS attack prevention] packetLiner supports to set up the volume based thresholds on IP ranges (a certain group of users) and services to recognize as Minor, Major and Critical events by pre-defined BPS. In the case a large amount of traffic over the threshold exceeds network, packetLiner detects it as DDoS attack and sends the alarm reports to defend.
Threat assessments help eliminate vulnerabilities by showing enterprises where the gaps exist. The more places you can look, the more secure you can be. Unfortunately, we often see organizations focus solely on humans as the "weakest link" in their security perimeter - all while undervaluing or ignoring tests of their technical systems. And those who do test their systems are likely to find that existing tools either lack effectiveness, like Breach & Attack Simulation (BAS) tools that only test against outdated attacks, or exceed their organization's available resources, in the case of expensive, manually intensive strategies like penetration testing or red-team hacking. It's important to stay within the limits of available resources and test both human and machine defenses with real-world, evolving phishing threats taken straight from the wild. With the IRONSCALES Platform's multi-tiered approach to email security and risk assessment, organizations can credibly identify and quickly resolve potential threats before they become real problems. Solutions include: The IRONSCALES Phishing Emulator' lets security teams safely test their email defenses using real and current threats - including those that were effective against Secure Email Gateways and other security tools in the past few hours from multiple threat intelligence sources including our decentralized threat protection network made up from our global customer community of top security analysts. Having the ability to gain With real-time knowledge of the vulnerabilities in their email infrastructure, companies can now move quickly to close these gaps based on real-time phishing emulations. In less than five minutes, Emulator can automatically run a comprehensive risk assessment, replaying suspicious phishing threats in a safe, pre-defined mailbox and then generating a full report of penetration gaps. And it goes beyond typical outdated BAS tools to test against modern, sophisticated phishing techniques like business email compromise, fake login pages, ransomware, and more. Phishing training and simulations are a great way to increase employee engagement with security initiatives while providing tangible, real-life scenarios to make them more conscious of phishing attacks. But not all tests are created equal. Generic phishing simulations might help you check a box for compliance, but they won't get your employees thinking like security pros. Instead, we focus on making simulations and user training more fun, realistic, and engaging with features that include customized, gamified training campaigns, real-world simulations, and a premium training video library by Ninjio. These tools allow security teams to figure out what attacks could be most effective against your organization or which employees might need some extra attention to get up to speed. In fact, that's why we regularly run realistic phishing tests in our own organization using this same tool. The best time to stop phishing emails is before they hit the mailbox, yet 25% of attacks get past existing defenses. If you don't have an accurate picture of your organization's email security, including human and technical defenses, then this statistic could spell bad news. Gaps in email security put you at a greater risk for phishing attacks that could cost millions of dollars. The IRONSCALES Platform takes a multi-dimensional approach toward threat assessments to paint the most accurate picture possible. Then our Al-driven, self-learning email security platform can help you resolve these threats before they become a bigger problem. Using our decentralized threat protection network, our platform helps you prevent, detect and remediate phishing attacks in a matter of minutes, not hours.
Late last year, we encountered an SMS Trojan called Trojan-SMS.AndroidOS.Podec which used a very powerful legitimate system to protect itself against analysis and detection. After we removed the protection, we saw a small SMS Trojan with most of its malicious payload still in development. Before long, though, we intercepted a fully-fledged version of Trojan-SMS.AndroidOS.Podec in early 2015. The updated version proved to be remarkable: it can send messages to premium-rate numbers employing tools that bypass the Advice of Charge system (which notifies users about the price of a service and requires authorization before making the payment). It can also subscribe users to premium-rate services while bypassing CAPTCHA. This is the first time Kaspersky Lab has encountered this kind of capability in any Android-Trojan. This article discusses Trojan-SMS.AndroidOS.Podec, version 1.23 (the version was identified from analyzing its code). The hash sums are: This version of the Trojan circulates in Russia and neighboring countries. \|Country\|\|Number of attempts to infect unique users\| The number of infections over time: Number of attempts to infect unique users Sources of Infection According to statistics collected with the help of Kaspersky Security Network, the main sources from which the Trojan in our study spreads are various domains with imposing names (Apk-downlad3.ru, minergamevip.com, etc.), as well as the servers of the popular Russian social network VKontakte (VK, vk.com) that are used to store users’ content. A pie chart of file infection sources As we see, in most cases the infection is sourced from the social network’s servers. Unfortunately, VK’s file storage system is anonymous, so there is no way to analyze how malware emerges from it. However, further research identified a number of communities that distribute Trojan-SMS.AndroidOS.Podec on this social network: (The Russian names of these groups refer to cracking Android games in some form) All the groups listed here were filled with similar content: images, links and messages. Each group is about one or more cracked games. The cybercriminals seem to be hoping that potential victims will be attracted by the chance to get free access to content that is usually paid-for. Nearly all messages on the groups’ walls are links leading to sites purportedly containing Android games and applications. The same is true for the “Links” section. In reality, the only purpose these sites served was to spread different versions of Trojan-SMS.AndroidOS.Podec. Eight groups in the social network with similar visual designs These groups have a lot in common: the way in which they are managed and designed (e.g. using keywords in place of descriptions, an abundance of simple broad-language messages characteristic of bots, etc.), the links they host to fake sites that seem to be copies of one idea. This suggests that black SEO (Search Engine Optimization) specialists were involved in distributing the Trojan. The above practices help bring links to the malicious resources (sites and groups) closer to the top of search engine results, attracting yet more visitors. All these clone communities have the same administrator, who is a VK user identified as ‘kminetti’. These communities are also advertised on that user’s personal page. The user’s account was created on 12 October 2011; in 2012, the account’s wall started hosting links to sites and communities spreading malicious applications for mobile devices. Examples of messages posted by the administrator of the malicious communities Earlier, this account was used as a bot hosting links to web resources to increase their citation indexes (CI). Examples of the posts placed by the communities’ administrator to increase CIs of third-party resources It can be concluded from all of the above that the VKontakte social network is the main vehicle for distributing Trojan-SMS.AndroidOS.Podec. The Infection Procedure The mobile Trojan sample that became available for Kaspersky Lab’s analysts masquerades as a popular application, ‘Minecraft Pocket Edition’. The file is 688 Kbyte in size, which may be an advantage in the eyes of inexperienced users with a slow and/or expensive Internet access. The official Minecraft application is 10 to 13 MB in size. When launched, the application asks for device administrator privileges. This step makes sure that neither user nor a security solution can subsequently delete the Trojan. If the user rejects the request, the Trojan keeps repeating it until the privilege is granted. This process effectively blocks the normal use of the device. Privilege escalation request When Trojan-SMS.AndroidOS.Podec receives the requested escalated privileges, the legitimate Minecrast app is downloaded from a third-party resource and installed on the SD card. This behavior follows the instructions provided in the configuration file that comes alongside with the Trojan; the same file specifies the link to the legitimate APK file. However, the configuration file does not always contain a link to the application; in this case, the Trojan simply stops any activities observable by the user after it receives the requested privilege escalation. Part of the configuration file containing the link to legitimate Minecraft installation file Then the Trojan deletes its shortcut from the apps list and replaces it with the real Minecraft shortcut. However, traces of the Trojan’s presence remain in the install apps list and in the device administrators’ list: The option of deleting the malicious app is deactivated. If the device user later seeks to de-escalate the Trojan’s privileges the machine responds with weird and unsettling behavior: the screen locks, then shuts down for some moments. When the screen comes back on the device displays the configuration menu and there is no evidence of any attempt to strip the malicious app of its admin privileges. Protection against analysis The cybercriminals apparently invested serious time and effort into developing Trojan-SMS.AndroidOS.Podec, as demonstrated by the techniques used to prevent code analysis. As well as introducing garbage classes and obfuscation into the code, the cybercriminals used an expensive legitimate code protector which makes it fairly difficult to gain access to the source code of the Android application. This protector provides code integrity control tools, hides calls of all methods and manipulations involving class fields, and encrypts all strings. Here is an example of protected code: This is the same code after the protection is removed: Managing the Trojan Trojan-SMS.AndroidOS.Podec’s activities are managed using C&C servers. The system works like this. First the Trojan contacts a C&C server via an HTTP protocol, and waits for an SMS with instructions. Trojan-SMS.AndroidOS.Podec has a main and a backup list of C&C domain names – a specific C&C server is chosen from the list following a random algorithm. If there is no response from that server within 3 days, a C&C from the backup list is used. This implements an adaptive algorithm to connect to a C&C server, which works even if specific domain names are blocked. The C&C domain names and the entire traffic (both HTTM and SMS) are encrypted with AES encryption algorithm in CBC/NoPadding mode with a 128 bit key. The encryption key and the initialization vector are originally located in the file fXUt474y1mSeuULsg.kEaS (the name of this file changes from version to version), located in the ‘assets’ folder of the app source. Most of the file content is junk; useful information is contained between tags, appearing in the form of [a]string[/a]. From the strings between tags, the required encryption parameters (the key and the vector) are obtained in an encrypted form. Then they are decrypted by simply replacing one substring with others. After decryption the commands form an XML document, in which the tags represent specific commands, and the contents of tags are command parameters. Below is the list of Trojan-SMS.AndroidOS.Podec capabilities implemented via commands: - Collect information about the device (cell phone service provider, IMEI, phone number, interface language, country and city, etc.) - Collect a list of installed applications. - Receive information about USSD. - Send SMS messages. - Set a filter on incoming messages. - Set filters on incoming and outgoing calls. - Display advertisements to the user (display a separate notification, open an advertisement page, start a dialog, and other ways to show commercial content) - Delete messages, as specified - Delete call records, as specified - Upload the source HTML code of a specified page to the cybercriminals’ server. - Perform a DDoS attack. Ramp up website visitor counters. - Subscribe the user to paid content. - Do a self-update. - Perform an outgoing call. - Export incoming messages according to conditions specified by C&C. - Delete an app, as instructed by C&C. Even a quick analysis of the Trojan’s executable code reveals an abundance of ways of working with HTML and HTTP. As well as features regarded as standard for this type of Trojans (e.g. sending and intercepting text messages, placing phone calls, manipulations with SMSs and call logs), Trojan-SMS.AndroidOS.Podec can also configure web page visits and send their code to C&C. However, this Trojan’s most interesting feature is its CAPTCHA recognition capability. A flow chart of Trojan-SMS.AndroidOS.Podec in operation is provided below. Thus, the web resource’s communication capabilities are the source of two different threats: - The Trojan contains functions with which one can launch a simple HTTP Flood DDoS attack. The associated strings in the configuration file are as follows: - One of the most dangerous capabilities in Trojan-SMS.AndroidOS.Podec is the use of configurable webpage visit rules, with CAPTCHA recognition supported. With this, the Trojan can subscribe the user to premium-rate subscriptions without the user’s knowledge or consent. This capability is unique to this Trojan, so let us review it in more detail. The resulting link is loaded; the function sleep() is called with the parameter ‘seconds’. This process is repeated as often as the ‘limit’ parameter specifies. The scheme used by the cybercriminals enables them to configure the frequency and number of access attempts; therefore, it can be used to ramp up web site visitor counters, thus generating profits from advertising and from partnership programs. There are two main models of subscribing to content on a web resource: - Pseudo-subscription. In this model, users visit a web resource and enter their phone numbers. An SMS is then sent, asking users to pay for the service by sending a reply message with any text. When users send that message, a certain amount of money is deducted from their phone accounts, depending on the specific service provider’s prices. These messages arrive automatically, and users make up their minds each time whether to send the reply message or not. It is for this reason that this model is often referred to as pseudo-subscription. - MT subscription. In this model, users enter their mobile phone numbers on a web page and receive an SMS with a validation code. Then users enter that code on the service provider’s website, accepting the subscription terms and conditions. After that, the service provider will automatically deduct the sum stipulated in the subscription terms and conditions from the subscriber’s account. In the Russian segment of the Internet, a number of partnership contracts are available that can aggregate this type of payments. This means that the cybercriminals do not have to directly deal with the cellular service providers when they create a service to which users can subscribe to paid content; partnership programs will do the agent’s job. Under this model, the revenue is lower for the service creators, but the financial transactions are more anonymous. Subscribing to paid services through a Trojan can be costly for users. In case of pseudo-subscriptions, one reply message may cost between $0.5 and $10. In case of MT subscription, the price in each specific case is agreed directly with the mobile service provider via the partnership program. The most dangerous factors here are that money is deducted 1) covertly and 2) on a regular basis. Users who are subscribed to several such “sources of content” may have to spend a lot of time and effort trying to find out where and how money from their accounts is going. Example of the Trojan in operation We were able to intercept Trojan-SMS.AndroidOS.Podec’s communication with its C&C server. This communication session unfolded as follows: - The RuMaximum.com website was accessed – this site provides online test services for users. To get their results, users have to subscribe to the site. - With a GET request, the Trojan imitates a user taking a test. Then it finishes with a link that looks like http://rumaximum.com/result.php?test=0&reply=0&reply=0&reply=0&reply=0&reply=0&reply=0&reply=0&reply=0&reply=0&reply=0. This URL leads to the following web document: - After the user enters a phone number, a unique “landing page” of the service provider is generated, demanding a CAPTCHA authentication and for a validation code that was sent to the phone by SMS. The Trojan fills out both fields and validates the subscription. Then, the user is redirected to the test results via the e-commerce system totmoney.ru. This test in Russian is “What type of dog is most like you?” Results of the test “What type of dog are you similar to?” “Yes, I am 18 years old or older, and I consent to the Terms and Conditions below. Enter your phone number.” Results of the test “What type of dog are you similar to?” You are a German shepherd, a versatile dog. You can guard the state border or help the blind across the street. You learn things easily and keep your head cool in any circumstances. A good manager too! The Trojan does all of these actions automatically using the configuration sent from the C&C. The victim, however, has no idea that any of this is happening. Paid subscription capability In the XML configuration sent from the C&C server, there is a field which subscribes the user to paid content. It looks like this: Let’s have a closer look at the configuration field: - verify is an array of strings with the separator “-S-“. It contains the information required to obtain the CAPTCHA value. - service is the accessed service; - search is an array of strings with the separator “-S-“, used to search for substrings in the link and to take a decision about the appropriate type of subscription depending on the search results; - images is not used in this version; - actions is an array of strings with the separator “-S-“. Contains the final links that the services follows to initiate/complete the subscription process; - type is request type; - source indicates whether the webpage’s source code should be sent to C&C; - domain: If the page’s source code should be sent to C&C, domain indicates the destination C&C. verify: if this field is not equal to zero, CAPTCHA recognition is required, otherwise further processing is done. This may contain the image file in base64 coding (done for processing static images and CAPTCHA), or an image ID; verify is the key of the service ‘http://antigate.com’ used to recognize CAPTCHA and required to login at the service; verify is the minimum image length, used for housekeeping purposes; verify is the maximum image length, used for housekeeping purposes; verify is the language of the symbols in the image. The webpage source code is required for cybercriminals to analyze the structure and to prepare an appropriate configuration for the paid subscription module. Also, this service provides source codes of webpages to ensure that the page’s code is received in a form that can be used to show it to the victim. This makes it easier for the cybercriminals to analyze the page and start the subscription. The function which completes the subscription to paid content is located in the class CustomWebView, which is inherited from the class WebViewClient. In it, the method onLoadResource was redefined (this method is used to get a link to the image), as was the onPageFinished method,which is used to post-process the loaded web-resource. Post-processing is based on analyzing the configuration and then visiting the required links with the help of the loadUrl function. When required, the CAPTCHA processor is called as well. Different partnership programs have different requirements from the design of a web resource where subscription tools will be hosted. For instance, there is often a requirement that for a CAPTCHA module to confirm that the request was not made from a bot. In most cases, the partnership program forwards the browser to the service provider’s site where users are prompted to enter a CAPTCHA code to confirm their subscription requests. As explained above, Trojan-SMS.AndroidOS.Podec’s key characteristic is that it can bypass CAPTCHA protection systems. Trojan Podec can subscribe users to premium-rate services while bypassing CAPTCHATweet The CAPTCHA processor communicates with the service Antigate.com which provides image-to-text manual recognition services. Here is what the service says on its web-page: Antigate.Com is an online service which provides real-time captcha-to-text decodings. This works easy: your software uploads a captcha to our server and receives text from it within seconds. In other words, the text from the CAPTCHA image is recognized by a person working for this service. According to the information Antigate.com provides on its website, most of its workers are based in India. Distribution of Antigate.com employees between countries The Trojan communicates with Antigate.com via an HTTP API service: a POST request is used to the send the image containing a text to be recognized; then, with the help of GET requests, the recognition status is monitored. The recognized result (if received in reasonable time) is inserted into the links from the ‘actions’ field of the received configuration. Then the links are opened with the help of the loadUrl()function. If the subscription mechanism requires SMS validation the Trojan uses the filter set by the cybercriminals to search for the message containing the validation code, and uses regular expressions to extract the code from there. The general subscription procedure General flow chart of subscription to paid content In general, the model of subscribing to paid content consists of the Observer SubscribeService which listens to the events as they occur in the HTMLOUT interface. When data (a downloaded page) is received from there, it is sent to C&C with the help of the class Submitter, which inherits the class AsyncTask. Also, SubscribeService accepts command parameters from the manager routine as input, initializes CustomWebView and starts to process the task with the help of SubscribeTask. SubscribeTask launches CustomWebView in which input parameters are processed, and decision is made about how the subscription should be performed. If required, CaptchaProcessor is launched, which is responsible for communications with the text recognition service and handling the requests that require validation code and the characters from the CAPTCHA image. From the analysis of Trojan-SMS.AndroidOS.Podec samples that arrived earlier, we can conclude that the Trojan is under ongoing development. The code is being refactored, new capabilities are added, and module architectures are being reworked. We suspect this Trojan is being developed by a team of Android developers in close cooperation with Black SEO specialists specializing in fraud, illegal monetization and traffic generation. The following evidence supports this theory: - The Trojan is distributed via the VKontakte social network employing social engineering tools; - A commercial protector is used to conceal the malicious code; - The scheme includes a complicated procedure of extorting money from the victim while bypassing CAPTCHA. Also, there are certain features in the code of the analyzed version of Trojan-SMS.AndroidOS.Podec which have not yet been used but which may reveal the malware writer’s further plans. For instance, there is an auxiliary function isRooted(), which helps to check whether the device’s owner has super-user privileges. This function is not used in the Trojan’s main code, so we can assume that a payload designed to exploit super-user privileges may emerge in future versions of the Trojan. Users of Kaspersky Lab’s products are already secured against all existing versions of Trojan-SMS.AndroidOS.Podec. Nonetheless, we recommend that users only install applications sourced from official stores, such as Google Play. The user should always be alert to cybercriminals’ tricks and avoid downloading cracked apps advertised as free of charge. If you download and launch a Trojan, you can potentially lose much more money than you may earn from not paying to purchase legitimate software. With acknowledgements to Mobile TeleSystems OAO, a GSM cell phone operator in Russia, and specifically to its experts in partnership programs traffic.
by Peter Kieseberg, Peter Frühwirt and Sebastian Schrittwieser (SBA Research) In recent years, standard end-to-end encryption protocols have become increasingly popular for protecting the security of network communication of smartphone applications, as well as user privacy. On the whole, this has been a good thing, but this reliance has also resulted in several issues related to testing. Software products have been becoming increasingly closely connected, particularly since the rise of mobile environments. Even programs doing menial tasks now require the user to go online, either to use cloud-based resources, or simply because the software provider does not charge users directly, but generates revenue by collecting and selling user preferences and advertising space. While the latter is often to be considered unethical, a study indicates that even those who complain are actually quite willing to give up privacy at a rather small price (see also for the ‘privacy paradox’ ). This increase in connectivity is accompanied by a larger attack surface open to potential malicious actors. Furthermore, the time to market of software products is getting shorter as the market is getting increasingly competitive, resulting in a virtual omnipresence of security issues in current productive software. Even more problematic is the fact that most of the issues currently impairing software security are not based on new scientific findings or large-scale (criminal) organisations conducting intense research for new vulnerabilities to put to criminal use, but rather exploit known weaknesses introduced either by ignorance, or by putting trust into well-known security measures that were never designed for the problem at hand. While the first problem refers to the problem of diminishing resources during the software development process and can be fixed using training, appropriate management and, most importantly, by setting aside resources, the latter is far more problematic and often touches at the very core of the design process of the software: Developers intrinsically assume certain aspects of their software and its use without further analysing the real attack surface. One very prominent example is the use of transport layer protection in mobile environments, with Transport Layer Security (TLS)[L1] being the de-facto standard for transport encryption. During a case study we analysed a set of mobile apps with respect to the usage of this protocol. We focussed on mobile environments since they possess some characteristics that are different from typical web-based applications. Condensing the results, we typically encountered two major issues with the use of TLS: - TLS was designed to provide secure end-to-end communication, always assuming the two endpoints to be trusted entities, i.e., removing them from the attacker model. Likewise, many software designers seem to assume that they possess full control over the client side of the software, which is clearly a problematic view in the case of mobile apps, where the client needs to be identified as a potential attacker. Many reasons for client side attacks can be found, e.g., in the realm of mobile messenger apps, users could try to impersonate other people in order to access or spoof messages. Particularly in apps that require the user to pay for the services provided, the motivation for attacking from the client side is rather straightforward. - Developers seem to rely far too much on the powers of TLS, even assuming capabilities, a protocol for transport layer protection can never hold up to. All TLS does is allow encrypted communication. ITLS cannot, for example, fix a protocol for authentication that is fundamentally flawed from a logical perspective. Nevertheless, we got the impression that in many apps TLS is seen as the silver bullet to fix any security-related issues. These issues should typically be uncovered during the test phase, but our study on many popular apps, including mobile messengers, mobile games, social networks and even online ticketing shops, revealed an astonishing number of insecure protocols and a general misconception of the attacker surface. Our testing approach for uncovering insecure protocols (see and ) relied on the fact that the smartphone the app is running on is under full control of us as adversaries, especially since any attacker having physical access to the phone can potentially manipulate hard- and software, including the operating system (or even run the whole app on a emulator without any actual smartphone). Since we controlled the smartphone we could make the phone route all of the (encrypted) traffic through a proxy, which was also controlled by us (see Figure 1). While TLS protected the communication from the phone to the proxy and from the proxy to the server of the service provider of the app, all the information is visible on the proxy, thus making every aspect of the protocols visible for analysis. For this approach, only standard tools are required, not only making this a good strategy for attacking apps, but also a viable and cost-effective measure for testing. Figure 1: Testing approach. In conclusion, it is clear that the topic of software testing needs far more focus, especially considering the new dangers of fully integrated and interconnected environments as envisioned by ubiquitous computing and the ‘internet of things’. Based on the experiments carried out in order to grasp the major problems and assess them, we will put our efforts into defining a testing approach suitable for interconnected environments, starting with the design phase of the software. Furthermore, we plan to support this approach with tools that will speed up the testing process for well-known and important protocols. Nevertheless, one of the major issues still prevalent with software testing, the issue of resources, cannot, in our opinion, be fixed on a technical level, but requires far more awareness on the management level. P. A. Norberg, D. R. Horne, D. A. Horne. “The privacy paradox: Personal information disclosure intentions versus behaviors”, in Journal of Consumer Affairs 41, no. 1, 100-126, 2007. P. Kieseberg,et al.: “Security tests for mobile applications – Why using TLS SSL is not enough”, in 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2015. R. Mueller, et al.: “Security and privacy of smartphone messaging applications”, International Journal of Pervasive Computing and Communications, vol. 11, 2015. Peter Kieseberg, SBA Research, Vienna, Austria
Why is the development of blockchain inseparable from security audits? With the rapid development of crypto technology, in addition to the explosive growth of market capitalization and user volume, blockchain security incidents are also showing a growing trend. The security of DAPPs, especially smart contracts, has become a pain point for the development of the whole industry. Conducting the security audit of projects is effective to solve the security problem of smart contracts. A smart contract can be simply understood as a computer program stored in the blockchain that can automatically execute transactions without a third party while ensuring that transactions are traceable and tamper-proof. For DAPPs, using smart contracts can not only take advantage of the cost and efficiency but also can avoid the interference of malicious acts on the normal execution of contracts with the characteristics of blockchain technology, which guarantee that the whole process of storage, reading and execution is transparent, traceable and tamper-proof. However, the open and transparent nature of the blockchain also allows all users to freely view the code of the smart contract, which may lead to all vulnerabilities, including security vulnerabilities, being visible. The most typical example is a hacker exploiting a vulnerability in the system, or a smart contract containing a critical vulnerability resulting in a vicious breach, making the smart contract code lost and causing financial loss to the user or the project party. Therefore, it is important to conduct security audits for smart contracts, which will help identify errors and vulnerabilities in the code and examine the program logic to identify the code architecture, logic and other potential security risks of smart contracts in time. MainStage is a decentralized and automated smart contract audit platform. The platform employs a variety of formal verification engines to quickly complete an automated audit of a smart contract and publish all potential risk points, vulnerability details, and relevant code locations to help developers or projects discover and improve their contract code. Compared with some centralized audit platforms, MainStage can provide an auditing service for startup projects or individual developers at a lower cost. Users can deploy their own DAPPs or smart contracts on MainStage for security auditing, and the auditing can be done automatically on a regular basis, which greatly reduces the labor cost of manual auditing and is more friendly to some newly launched or underfunded projects. For users and projects with higher security requirements, MainStage can also provide a detection engine for in-depth code auditing and logic vulnerability detection before smart contracts launch. As a decentralized auditing platform, MainStage also allows users to participate in the auditing process. Users can deploy their own test codes to conduct security tests on projects, and these test codes generate special NFTs on MainStage. Users can also get additional $GEON rewards by staking NFTs. This incentive mechanism will encourage more technicians to participate in the development of the platform test codes and add more test cases, thus improving MainStage platform tests and achieving better auditing results.
Graphic Era University IP traceback involves identifying the actual source of a packet across the Internet. By identifying the real source address in packets, network security system can smartly protect the victim hosts and mitigate the attacks. Packet marking is the most important method of source identification using IP traceback and there are many variations. In this paper, the authors propose a modification to their previous capable IP traceback scheme, Deterministic Router and Interface Marking (DRIM), to handle fragmented traffic as well. The modification introduces nominal additional bandwidth overhead, with no additional memory requirements and processing overhead on the DRIM-enabled interface and also reduces the problem of false positives.
Information technology security is an unending challenge for both the private and public sectors. Private sector firms have their own security protocols and commercial motivations to ensure security — as well as the obligation to meet appropriate government regulations. Federal agencies not only have to worry about operational difficulties that stem from security breaches, but also about meeting the requirements of the Federal Information Security Management Act of 2002 (FISMA). The act requires federal departments and agencies to perform periodic security assessments; provide information security training to employees and contractors; and implement policies and procedures to reduce security risks to an acceptable level. IT vendors supplying hardware and software to federal agencies also have to be aware of FISMA requirements and other government-related security measures. A currently vexing federal security issue is detecting and stopping malicious attacks on computer networks. Malware, or malicious code, is a common tool for breaching computer networks. The National Institute of Standards and Technology (NIST), an agency within the U.S. Commerce Department, has just issued a draft updated guidance document for dealing with such attacks. NIST issued the guidance in response to a FISMA directive to the agency to continuously assess and develop federal IT security standards. NIST is seeking comment from the IT community — both public and private — on the guidance. “Malware threats in the past tended to spread quickly and were easy to discover,” said Karen Scarfone, a contributor to the NIST draft. “But today’s malware threats are stealthier, specifically designed to quietly, slowly spread, gathering information over extended time frames and eventually leading to loss of sensitive data and other problems,” she said. NIST’s draft on Intrusion Detection and Prevention Systems (IDPS), describes software that has become a necessary addition to the security infrastructure of many organizations. IDSPs record information about observed security-related events, notify security administrators of the events that should be analyzed further, and produce reports for evaluation. The draft addresses four types of IDPS technologies: network-based, wireless, network behavior analysis and host-based. NIST’s general recommendations call for federal agencies to do the following: - Adopt an approach to malware prevention based on the attack vectors that are most likely to exist within their operating environment; - Implement awareness programs that include guidance on incident prevention, including the ways that malware enters and infects hosts and the importance of users in preventing incidents; - Develop vulnerability mitigation capabilities to help prevent malware incidents including security automation technologies with checklists, patch management and “host hardening” measures; - Utilize measures to detect and stop malware before it can affect its targets, including the use of antivirus software; intrusion prevention systems, firewalls, content filtering and inspection; and application white listing. In addition, federal agencies should alter the defensive architecture of their hosts’ software to help mitigate incidents that still occur. This includes using isolation techniques such as browser separation and other segregation measures. All federal agencies “should have a robust incident response process capability that addresses malware incident handling,” NIST said. Don’t Forget Desktops and Laptops “IDPS for wireless is an important type for all organizations to have because of the growth of mobile devices and employees’ desire to use their own wireless device for work,” said Scarfone. While many agencies and companies are going mobile, it is still critical to protect desktops and laptops, NIST said in releasing a separate draft of malware guidance addressing such computers. It gives background information on the major categories of malware that afflict desktop and laptop computers, and it provides practical guidance on how to prevent malware incidents, and on what to do when a system is infected. The NIST guidance updates for IDPS, desktops and laptops should not result in major changes in agency procurement of IT solutions, but should simply serve as awareness documents related to increasing malware threats. “NIST is a non-regulatory agency and does not require or track agency cybersecurity implementations,” Murugiah Souppaya, research associate at NIST’s computer security division, told the E-Commerce Times. “Based on our discussions with agencies and the results of the open comment period, we hope to validate our understanding that adopting the new guides will not lead to overhauls of existing systems,” he said. NIST hopes to get feedback from a wide range of government, commercial and other organizations. “This helps to validate that our recommendations are sound for all organizations, regardless of sector, size, or composition,” Souppaya said. Smaller Targets Curb Attacks One industry source likened NIST’s approach to that of general guidelines for good health: a prudent diet and regular exercise, with the NIST documents both informative and useful at a basic security level. “I think the recommendations start from the wrong end of the problem,” Tim Keanini, chief research officer for nCircle, told the E-Commerce Times. “While intrusions are inevitable and agencies need to detect them, making the target surface as small as possible prior to intrusions is a far more important strategy because it raises costs to attackers, making intrusions far more difficult,” he said. “To be successful, malware has to find a point of entry. While it may seem like we can never completely secure every possible way to exploit a machine, the sad news is that most malware attacks exploit well known, easily detectable vulnerabilities,” Keanini added. “This means that we need to become far more diligent about security basics and put at least as much focus into about shrinking the target surface through automation as we do in detecting and responding to threats,” he explained. “Targeted attacks that leverage advanced malware and ‘weaponized malware’ are on the rise and getting more sophisticated by the day,” Andrew Hoerner, director of product marketing for the network security business Unit at McAfee, told the E-Commerce Times. A proactive approach to network security is preferable to a reactive approach, he agreed. “By the time the attack code crosses the wire, it is too late.” In terms of the NIST initiatives, McAfee supports a proactive, collaborative approach between the private and public sectors that utilizes the strongest strategies and action plans to address cybersecurity, Hoerner said. “We believe information sharing and, ultimately, codifying federal requirements can help meet both current and future challenges in these areas,” he concluded. NIST is seeking comment on the malware draft documents by Aug. 31. “Hardware and software must work together in order to have an effective defense,” Keanini said. “Vendors can supply the technology, but unless the agency’s IT security staff understands the guidance, implements the technology appropriately, and strives to constantly refine and adapt their security processes to combat evolving threats, the security features in hardware and software don’t mean very much.”
Seems like my issue might have to do with section 6.6 of the Guide to Securing Microsoft Windows XP Systems for IT Professionals: A NIST Security Configuration Checklist. Seems like there are restrictions set on executable files. On my computer, with the NISTWinXPPro_enterprise_R1.2.1.inf template, I get access is Denied with runing login.bat files as a local user, but not as a local administrator. 6.6 File Permissions This section provides general instructions regarding setting permissions through file system access control entries (ACE)102 and access control lists (ACL) for Windows XP.103 The NIST templates and GPOs restrict access to dozens of executables, protecting them from unauthorized modification and usage. Additional custom settings may be added that are specific to the environment in which the Windows XP machine resides. Changes to an ACL for a specific resource, such as a file or folder, can be made using one of three possible methods: Open the Properties window for a resource from its context menu and click on the Security tab. It displays the privileges that each user or group has to the resource. The Advanced button can be used to set more granular permission rights and additional settings such as file auditing and the owner of An ACE is an entry that binds a security identifier (SID) to a set of permissions within an ACL. Once file permissions are applied, there is not an automatic way to undo them or otherwise return the files to their previous permissions. Additional procedures, such as recording the original file permissions before applying new ones, may be needed to provide an undo capability. The same is true for the registry permissions described in Section 6.7. Use the utility cacls.exe found in %SystemRoot%\system32.104 This is a command-line interface used to set file ACLs, but it does not set Windows XP security descriptors. Use the MMC Security Template snap-in to apply settings from a template. Windows XP uses an inheritance model for assigning ACEs. An objects ACL can contain ACEs that it inherited from its parent container. For example, a file in an NTFS filesystem can inherit ACEs from the directory that contains it. In addition, an ACE that is directly applied to a filesystem object is given a higher priority than an inherited ACE. The directly applied ACE overrides any conflicting inherited
In many malware cases, the infection method can be far more elaborate than the actual malware being installed. This is the case with Newscaster, a campaign believed to have Iranian origins and targeting US defense contractors, high ranking military officials, and government officials. With an infection vector so involved, and malware as simple as it is, this campaign was able to avoid detection since it began in 2011. Spreading the news In a report for the recently exposed attack dubbed “Newscaster” by iSIGHT Partners (documented here) highlight how social networks, combined with social engineering efforts continue to be a highly successful attack vector. The level of effort, time and detail expended, combined with the profile of the victims was significant. Fake news site that was used in the attacks The report details how senior targets in the military, diplomats, defense contractors and journalists all became victims of a well-engineered social network attack that leveraged a fictitious news website “NewsOnAir.org” utilizing fictitious reporting personas that interacted with the victims over LinkedIn, Facebook, Google+, and Twitter. It is believed that the core group of attackers are Iranian. Potential Facebook site used in the attacks The usual approach to social engineering attacks on social networks is to lure users into providing credentials or opening files/links intended to compromise their computers. The attacks in this campaign came from an attacker posing as a legitimate persona, sharing common interests or business goals. The convinced victim will "connect" with the attacker, whom they believe they know, or has similar interests or business goals. This will then lead to dialog with the attacker masquerading as a peer. Once the connection is made, the attacker will send “spear phishing” emails or direct messages containing links to the victim through the chosen platform (Facebook, Google services, LinkedIn, Twitter). Fake Google+ account with Typo Google+ Account has 142 people/organizations in their circles including several government and politicians which contribute to its 'clout'. The victim is far more likely to click any links that have been directly shared with them especially if blended with other legitimate dialog related to the common interest. The payload of these messages ultimately results in data loss or theft – typically delivered as spear phishing emails or instant message communication to deliver data stealing malware or user supplied credentials. The website was made to look legitimate by providing news feeds from legitimate sites, but with the bylines of the articles posted changed to the fake personas used by the attackers to increase the legitimacy of any interaction with the victim. The Cylance research team discovered 90 samples related to this campaign, 12 of those still without any detection after an extended period of time. Many of these samples leveraged Botnet / IRC techniques to control the victim PC. The sample would have likely been delivered via a phishing email. The screen shot below shows the “Flash player” executing. Let's take a deeper look into this sample so we can better understand the leverage gained from these infections. The samples fall into two primary groups, installer and bot. The installer portion is the result of a file binder which executes the bot and bait at the same time. The name of the file binder being used is "SetupEx". The bait typically is some form of product installer or funny video. When we first execute this sample installer, we can see the bait application running. The bait in this installer is a Flash video The bot component is the core of the payload. Its operations are simple but effective. It does have methods implemented to avoid detection, but they are not advanced. We can observe some of these methods statically. For instance, there are a large number of encoded strings in the binary. The encoding method is inverting the alphabet (for instance 'a' becomes 'z'). To save time, I developed a script to automate this decoding. You will be able to see strings such as the remote domain for the IRC server, the channel being used on the IRC server, its password, etc. This information is not stored in a rigid format, so you still must extract them from the results of this script. Here is some sample output when decoding the mutex name. bwall@research:~$ echo "zuLmvXlkbNfgvc" \| python alphareverse.py Example usage of decoding script We can use some of the encoded strings to create an effective YARA rule (detects 85 of 90 samples). $needed01 = "PON\\HLUGDZIV\\Nrxilhlug\\Drmwldh\\XfiivmgEvihrlm\\Klorxrvh\\Vckolivi\\Ifm" $needed02 = "zuLmvXlkbNfgvc" $needed03 = "f:\\filebuildernew version\\builder c++\\setupex\\debug\\setupex.pdb" nocase 1 of them YARA rule for detecting Newscaster binaries This bot stores its configuration in the current user's TEMP directory, creating a subdirectory named "System". It stores a mutated instance of itself in the ProgramData directory, creating a subdirectory named "Windows Update". It names this instance "isass.exe" but with a capital "I" to appear as if it were 'lsass.exe', a critical part of the Windows operating system. All instances appear to use the same mutex, "afOneCopyMutex", which also happens to be part of the above YARA rule (albeit in its encoded state). Disassembly showing mutex name being decrypted and used by CreateMutexA For more information on the malware's non-interactive behavior, see the Malwr analysis here. Command and Control This bot uses IRC for its command and control protocol. It also appears that a custom IRC daemon is being used, as it supplies very little information back to connecting bots. I setup an IRC server and used the name "AF" for my client. The sample being used only accepts certain commands from other users with names starting with "AF" or "AS_". The IRC channel is configurable, and may be different for different samples. The connection to the IRC server is delayed after the start of the bot. Disassembly of code section checking checking name of command's sender The connection to the IRC server happens after attempting to disable the UAC in Windows, which would allow for easier privilege escalation. From reverse engineering, we can see the accepted command, although a few do not perform any operations. The "VER" command obtains version information from the bot. The "HI" command invokes a polite response. The "!CMD" command executes commands. The "EXEC" command gets the path of the executing binary. The "!DSF" command will upload a selected file to a select IP and port. An instance of netcat was able to capture the uploaded data. Here is a truncated hexdump from the receiving server. A similar command, "!UP", uploads the selected file to an HTTP server. I also used a netcat server for this. Here is the truncated hexdump of the information uploaded. The "KILL" command shuts the bot down. Cylance PROTECT is able to run in both a blocking and monitor mode. In this example we are running in monitor mode so we can gain more visibility into the threat. Running the application causes CylancePROTECT to discover two threats. The detection of two files is due to the second dropped file. In the PROTECT console we are able to see two active threats on the client, and both files detected. CylancePROTECT does not rely on detonation analysis, but it is able to provide this data in the console to assist in forensic analysis. Drilling down via the console, we can see that after initial execution, the dropped file is running. Clicking on the “endend.exe” allows us to drill in further on the specific sample. By clicking the Detailed Threat Data button, the CylancePROTECT Administrative console allows us to get further details on the threat: in this case, dropped files. Detailed Data -> Network provides us a view into the hosts that the malware is attempting to communicate with. The PROTECT console also allows us to see the File, Mutex and Registry keys that the sample attempts to generate or interact with during detonation, aligning to the research earlier in the blog post. Using Cylance V, our detection and forensics tool, we can see that all 90 samples we were able to source are detected, and of that set, at least 12 samples still do not have solid AV industry detection. The Newscaster campaign, while not technically advanced, was elaborate and extensive. It managed to go undetected from at least 2011, and some of the malware it produced continues to go undetected by signature-based detection. The mathematical detection provided by Cylance, even with no prior knowledge of this malware, had no issue in detecting 100% of these samples. Sample distribution with relations computed by ssdeep
Before you start Objectives: learn how to use security templates to apply security settings in XP. Prerequisites: no prerequisites. Key terms: setting, group, password, policy, local, analysis, database, import, compare, member When we open the ‘templates’ folder, we will see several files with .inf extension. Before the ‘.inf’ extension we can see ‘ws’ or ‘dc’ added to the name of the template. ‘ws’ indicate that that template is intended for a workstation. ‘dc’ indicate settings for the domain controller. Settings for servers will have ‘srv’ at the end of the template name. We start off with a basic set of templates. Those are basic security settings that are applied by default during the installation of the system. In addition to that we also have, the Secure Templates. We also have High Security Settings in which we start to manipulate with user rights. We also have a temple called Compatibility Templates. The common one we will see here is Compatible Workstation or comptws.inf which allows us to apply a security template that is consistent with the previous versions of Windows. Since previous versions are not able to use all of the security settings that we have in Windows XP, we can set those back so that we can still maintain compatibility. The first tool that we can use is the Security Analysis and Configuration in Microsoft Management Console or MMC. This tool gives us two components which allows us to analyze our security based on our templates. We can select a template, open up a database using that template and then analyse our computer. After the analysis it will show us everything that meets and exceeds the requirements of the template. Anything that doesn’t meet the requirements of the template will be illustrated with the red X. If we want to apply that template we can go to the configuration portion of the Security Analysis and Configuration tool which will allow us to apply all that settings to the computer. When applying settings, if existing setting meets or exceeds particular setting, then it does not make any changes. Another tool that we can use is ‘secedit‘ command line tool which basically allows us to do same thing as with Security Analysis and Configuration tool. We can use secedit command with the /analyse switch to analyse our settings or we can use the/configure switch when we want to make changes to our settings. We can use secedit /export to export database settings to a template. When applying high security templates, the Administrators group is reset. Administrators and Power Users group are reset to default members, so if we have a lot of members in that groups it can be an issue. After applying the template we should check those groups and add members back as necessary. Another issue comes up when we move between various templates. If we have applied a high security setting, and after that we want to go back to the basic settings, we have to clear the existing template first. Remember, when we apply our templates, if particular setting meets or exceeds template setting, it will not make any changes. We will compare the security settings in Local Group Policy on our computer to the settings in a predefined template. In that way we can see what custom settings are modified on the local computer. To do that we need to perform three general tasks. First we need to configure MMC to work with security settings, second we have to import the template database, and third we need to compare the template with the local settings and view the results. Let’s start by creating the MMC. We’ll go to the Start Menu, in the Run command type in ‘mcc’ and hit enter. On the File menu, select Add/Remove Snap-in, select and add the Security Configuration and Analysis Snap-in. Image 271.1 – Security Configuration and Analysis Now that we have our snap-in set, we can compare the security settings on the local system with those in the template. Now we need to create a new database and import the template settings. Let’s right-click Security Configuration and Analysis and select Open Database. We will name it CompareSettings and click Open. Image 271.2 – Database Next, we have to import our template, that is, we need to select the template that we are going to compare to the local computer. Image 271.3 – Templates All those files are actually stored in ‘c:\windows\security\templates‘ folder. In our case we will select ‘securews.inf‘ and click Open. At this point we need to compare the settings in the template with the settings on the local computer. To do that we will right-click ‘Security Configuration and Analysis’ and select Analyze Computer Now. Click OK to accept the path to the error log file. The following window will appear. Image 271.4 – Analysis If we browse the the Account Policies and then Password Policy, we can see the settings from our database and the current computer settings. Notice the red X and the green check mark. A red X tells us that the setting on the local computer does not match the setting in the template, while the green check mark tells us that the settings do match. Notice that we have two columns for details. Those columns are the Database Setting (template setting) and Computer Setting (current setting applied on the computer). For example in our case, notice that the minimum password length in the template is 8 characters while current setting is 0 characters, which basically means ‘no restriction’. To apply all those settings we can right-click ‘Security Configuration and Analysis’ and select ‘Configure Computer Now’ option. All settings will then be applied. To check our new settings we can go to our Group Policy Editor and navigate to the, for example, Password Policy. Image 271.5 – Password Policy Notice that our settings now include minimum password length of 8 characters. While we can manually edit group policy settings to achieve the desired configuration, we can simplify the process by importing a predefined template. Windows XP ships with several predefined templates. We can also import our template while we are in Group Policy Editor. Let’s say that we want to revert our changes to the original settings set during installation. To import a policy, we will right-click Security Settings and then select Import Policy in Group Policy Editor. Image 271.6 – Import Policy Compatws.inf provides Windows NT 4 compatible settings. Templates starting with ‘secure‘ like securedc.inf and securews.infare used to increase the security for workstation or domain controller. Securedc.inf is used for domain controllers andsecurews.inf is used for workstations. Hisecdc.inf and hisecws.inf increase security even further. The ‘setup security.inf‘ is the default security that was created when we installed Windows XP. Let’s import ‘setup security’ to revert to the defaults. We will select it and click Open. Image 271.7 – Setup Settings Notice how our password policy has changed. Now they’ve reverted to the default security settings. Our password history is zero and our maximum password age is 42 days instead of 30. Also our minimum password length is zero characters instead of eight. To edit existing templates we can use the Security Templates MMC snap-in. ‘Setup security.inf’ configures the system with the original settings applied during installation. ‘Securews.inf’ enhances security settings that typically do not affect application compatibility. It defines strong password, lockout, and auditing settings. It also restricts rights granted to anonymous users. ‘Hisecws.inf’ secures a workstation as much as possible. It forces NTLM v2 between server and client, and removes all members of the Power Users group. It also removes all members of the local Administrators group except for the Domain Administrators group and the local Administrator account. ‘Compatws.inf’ relaxes the security privileges of the Users group to allow them to run non-user certified applications (applications that are common in previous Windows versions). It also Removes all members of the Power Users group. Paths that are mentioned in this article - c:\windows\security\templates – folder where we can find some predefined security templates
PSCrypt is a ransomware based on GlobeImposter 2.0, a ransomware strain that’s been around for more than a year, and has evolved from the Globe ransomware family. Ukrainian users have been aggressively targeted during the past month with PSCrypt after XData and NotPetya. The PSCrypt Ransomware Trojan is distributed to users via spam emails loaded with a macro-enabled Microsoft Word file. The document may be proposed to users as an invoice, order confirmation and message from a friend on a social media service. Either way, the file acts as an installer that includes a script which is loaded in Windows and issues commands that result in the installation of the PSCrypt Ransomware. During data encryption, it appends .pscrypt file extension and makes data impossible to open. Once it’s done, this crypto-malware creates and saves a Paxynok.html file into every folder that contains encrypted data, including the desktop. The ransom note carries victim’s personal identifier and a message from cyber criminals which says that all files have been encrypted by PSCrypt. The letter suggests that the victim must buy Bitcoins at LocalBitcoins, Coinbase or XChange and then transfer a required sum to a provided Bitcoin wallet. Cyber criminals ask to write them a letter via [email protected] email address which is also provided in the ransom note. The crooks suggest that their “operator will give the further instructions.” According to the ransom note, victims have to pay 2500 hryvnia (approximately 96 US dollars) in order to decrypt corrupted files. The cyber criminals provide an unusual ransom payment method – paying the ransom via IBOX terminal. Malware not only encrypts files but also makes the system vulnerable. It might make various modifications in the system, create new or delete existing registry entries, and even open the backdoor to other cyber threats. Thus, having this malicious program installed on a device might lead to even more serious problems. It goes without saying that you should first make a copy of data back up done by Max Total Security and then format PC. Reinstall new operating system and then do data recovery.
With more and more companies using virtualization, such as Microsoft Virtual Server, Server 2008 Hyper-V or VMWare, in their environments these days you may end up in the following situation: 1. Customer wanted to roll back one of his DC’s in his test environment to basically “back out” of some changes that had been made recently. This was a single domain forest that consisted on two Domain Controllers. Both of the DC’s were running Windows 2003 SP2. 2. Virtual Machine snapshots were being taken instead of normal system state backups. 3. They restored one of the DC’s from one of the snapshots. 4. Replication was broken. Full Article: DS team Blog
Overview: DMARC.org (Domain-based Message Authentication, Reporting and Conformance) is an unincorporated working group that develops Internet standards intended to reduce the threat of email phishing and to improve coordination between email providers and mail sender domain owners. Members comprise email providers (AOL, Gmail, Hotmail, Yahoo! Mail), financial institutions and service providers (Bank of America, Fidelity Investments, PayPal), social media properties (American Greetings, Facebook, LinkedIn) and email security solutions providers (Agari, Cloudmark, eCert, Return Path, Trusted Domain Project). Specifications: Available at: http://www.dmarc.org/specification.html IPR Policy: Not available Current Status: Active Last Updated: November 18, 2013
New Alerting Technology Protects Businesses from Cyberattacks BrevAll Technologies is deploying a Software-as-a-Service (SaaS) alert technology to help its customers monitor, protect and manage their employee’s access to commonly-used business applications. BrevAll’s new SaaS alert technology monitors upwards of 35 different applications and gives SMBs real-time alerts and reporting capacities. Additionally, it automatically responds to issues that require attention before the customer has initiated any action. “We’re excited to deploy this technology because it empowers our customers to immediately be notified of any issues so we can rectify them immediately,” said Paul Enloe, CEO of BrevAll. “We’ve all heard about the acceleration of ransomware attacks in the news lately; the real problem behind any breach is not knowing the extent of the breach for a long time period. With this technology, our customers will know instantaneously if a breach has occurred as we thwart attacks on their behalf.” While technologies like G Suite, Salesforce, Slack, Dropbox, Office 365, or Box have become ubiquitous in the modern workplace, unfortunately, they expose networks to certain vulnerabilities that can be prevented. While BrevAll’s SaaS technology monitors and alerts SMBs on various cyberattack methods, many businesses are undereducated on the six most common attacks. Most Common Cyberattack Methods: Brute Force Attacks– this is when cybercriminals use automation and scripts to guess passwords. Typical brute force attacks make a few hundred guesses every second, taking advantage of simple passwords that use common expressions like ‘user123’ or ‘password1,’ and can be cracked in minutes. Logins From Unauthorized Countries– these types of breaches can be spotted by various indicators such as a VPN connection from an unknown device or anonymous proxy, an abnormal amount of data uploaded during a VPN session, an increase of company-related data files accessed, multi-factor authentication (MFA) from a new device, or too many failed VPN logins. Outdated File Shares From OneDrive/Google Drive/Dropbox/Etc. (Orphaned Links) – these occur when attackers overtake expired, stale, and invalid external links on credible websites, portals, or applications so that they can repurpose them for fraudulent activities. Data Exfiltration – this is when any malicious actor targets, copies, and transfers sensitive data outside of a company’s network, which can be used to extract a ransom or be offered to a competitor for a bribe. Confidential Files Viewed– when businesses are immediately notified as to which users are accessing confidential. A telltale sign that a cyber attacker is poking around in a network that they shouldn’t be meddling in is when confidential files are frequently being viewed. Security Group and Policy Changes– this is a means to make it easier for a hacker to break in and cause a deeper extent of damage to a business or organization. Yet, SaaS alert technology can be configured to send off an alert to ensure that the company controls any changes being made to the security group.
HID vs NID: Choosing the Right Intrusion Detection System for Your Cybersecurity Needs Intrusion Detection Systems (IDS) are crucial in safeguarding your organization from cyber attacks by identifying and responding to potential threats. However, not all IDS are created equivalent, and selecting the IDS that best meets your organization's unique cybersecurity requirements is essential. In this article, we'll compare Host Intrusion Detection Systems (HIDS) and Network Intrusion Detection Systems (NIDS) and help you determine which form of IDS is best for your organization. HIDS stands for Host Intrusion Detection System. A Host Intrusion Detection System (HIDS) is a software application that monitors the behavior and activities of individual network devices or endpoints. HIDS detects anomalous activity by analyzing system logs, files, and other host-related data. HIDS primarily aims to detect unauthorized access or nefarious activity on a device. HIDS can operate in both reactive and proactive modes, meaning they can respond to a threat after it has occurred or take preventative measures to thwart an attack. NIDS stands for Network Intrusion Detection System. A Network Intrusion Detection System (NIDS) is a hardware or software system that monitors network traffic to detect malicious activity. NIDS analyses network traffic to identify behavior patterns that indicate a cyber attack. NIDS can operate in passive and active modes, meaning they can monitor traffic without interfering or taking active steps to prevent an attack. What differences exist between HIDS and NIDS? HIDS and NIDS are dissimilar in several ways, including their scope, concentration, and the type of data they analyze. HIDS operates at the device level, focusing on individual network devices. On the other hand, NIDS works at the network level, focusing on the entire network and all connected devices. The HIDS focuses on the activities and behavior of individual devices within the network, whereas the NIDS focuses on the network traffic. HIDS examines host-related data, such as system records and files, to detect suspicious activity. To detect malicious activity, NIDS analyzes network traffic data, including IP addresses and packet metadata. When should HIDS be chosen over NIDS? HIDS is an ideal option for organizations with limited devices wanting to monitor their activities more closely. HIDS is also helpful when identifying the root cause of a security incident. HIDS can detect insider threats and other internal security violations that NIDS, which only monitors network traffic from the outside, may miss. In addition, HIDS is more effective at detecting malware that has already infected a device or system, as it concentrates on the activities and behavior of individual devices. When should NIDS be chosen over HIDS? Organizations wanting to monitor network traffic should implement NIDS. NIDS is helpful when guarding against external hazards like DDoS attacks, phishing, and malware. NIDS can detect and prevent these attacks by analyzing network traffic and identifying behavioral patterns that indicate a threat. Additionally, NIDS can provide enhanced network visibility, enabling organizations to monitor all network traffic and identify potential security hazards. Choosing the appropriate IDS type is crucial for ensuring your organisation's security. Both HIDS and NIDS play a pivotal role in identifying and responding to potential threats, but their scope, focus, and data analysis methodologies are notably distinct. When deciding between HIDS and NIDS, you must consider your organization's specific requirements, such as the number of devices, the volume of network traffic, and the types of security hazards you will most likely encounter. HIDS is more appropriate for organizations with limited devices and a higher need for device-level monitoring. In contrast, NIDS is more suitable for organizations with many devices and a greater need for network-level monitoring. Ultimately, the best option depends on your organization's requirements and cybersecurity objectives. Notably, HIDS and NIDS are not mutually exclusive, and many organizations employ both types of IDS for comprehensive threat detection and prevention. In this situation, HIDS and NIDS can collaborate to provide layered security against internal and external threats. In conclusion, choosing between HIDS and NIDS will depend on your organization's specific cybersecurity requirements and objectives. By understanding the distinctions between these two types of IDS and the circumstances in which they are most effective, you can select the most appropriate form of IDS to protect your organization from cyber-attacks. Whether you choose HIDS, NIDS, or both, it is essential to maintain a vigilant and proactive approach to cybersecurity to safeguard the data and assets of your organization. Visit https://www.roycemedia.com/nids-hids to learn more about RoyceMedia’s NIDS and HIDS offerings.
US 20030045271 A1 A method of reducing access to communication system resources by a fraudulent Mobile Station (102). The Mobile Switching Center (104) sends a message to the Authentication Center (106) to invoke a first authentication procedure. The Authentication Center responds with a message including the results of the first procedure, and possibly, parameters for performance of an additional authentication procedure. Based on the contents of the message, the Mobile Switching Center decides whether to delay call setup until after the additional authentication procedure has completed successfully or whether to initiate call setup in parallel with the additional authentication procedure. 1. A method of reducing fraudulent access to communication system resources by a mobile station, the method comprising the steps of: transmitting a first message to invoke performance of a first authentication procedure; receiving a second message containing a first parameter indicating a status of the first authentication procedure and containing at least a second parameter associated with a second authentication procedure; and determining whether to delay the mobile station access to the communication system resources until the second authentication procedure has completed successfully. 2. The method of determining whether the first parameter is an AuthenticationFailureEvent parameter; if the first parameter is the AuthenticaitonFailureEvent parameter, delaying the MS access to the communication system resources until the second authentication procedure has completed successfully. 3. The method of determining whether the first parameter is a Deny Access parameter; if the first parameter is not the Deny Access parameter, determining whether the first parameter is an AuthenticationFailureEvent parameter; if the first parameter is not the AuthenticationFailureEvent parameter, initiating call setup before the second authentication procedure has completed. 4. The method of determining whether the second authentication procedure is successful; and if unsuccessful, discontinuing call setup. 5. The method of 6. The method of The present invention relates generally to the field of communication systems, and more particularly, to a method for reducing fraudulent system access by a mobile station in a communication system. To prevent call originations by fraudulent mobile stations (MSs), wireless system operators may choose to authenticate a MS using a procedure generally known as Global Challenge Authentication (GCA). During this procedure, the MS uses a random number (RAND) that is broadcast on the control channel to generate an authentication result (AUTHR) that uniquely identifies the MS based on shared secret data (SSD) stored in the MS. The MS uses the AUTHR and a portion of the random number (RANDC) in the call origination attempt and a comparison is made between the received AUTHR and the AUTHR generated by the Authentication Center (AC) using the same input parameters used to determine the authenticity of the MS. GCA has some potential drawbacks including the potential inability of the serving system to determine the random number from the RANDC received from the mobile; the possibility that the mobile may not include the appropriate authentication parameters in the origination; the possibility that the authentication results may not match for a valid MS due to the SSD in the MS and the AC becoming out of synchronization; and attempts to gain fraudulent system access using a replay scenario can go undetected. As a result of these drawbacks, the wireless system operator may choose to authenticate the origination by performing a unique challenge or SSD update following a global challenge failure or as a follow up to global challenge authentication. For a mobile origination, the unique challenge and/or SSD update operations are performed on the traffic channel assigned to the MS and may be performed prior to, or in parallel with call setup. If the operation is performed prior to call setup, the authenticity of the MS can be determined before the call is routed at a cost of delaying call setup. If however, the operation is performed in parallel with call setup, no delay is encountered. However, there is a risk that the call may be answered before the operation is complete which could result in fraudulent usage of system resources if the MS fails the authentication. Further, if the origination was performed to update the subscriber profile via a feature code, a fraudulent MS could update the valid subscriber profile. This could result in a loss of revenue for the wireless system operator if, for example, a fraudulent MS activated call forwarding and registered a long distance number as the forwarding number with the intention of obtaining free long distance service. It has been found that in order to minimize call setup delay while preventing fraudulent system access, a good approach is to utilize GCA and perform subsequent traffic channel authentication operations based on the outcome of the global challenge. If the GCA is successful, any subsequent authentication operation (e.g. SSD update) should be performed in parallel with call setup because the authenticity of the MS has been verified and there is no reason to delay call setup. However, if GCA is not successful, a subsequent authentication operation, if any, should be performed prior to call setup because the authenticity of the MS has not been verified. Chapter 6, sections 4.4.3 and 4.4.4 of Cellular Radiotelecommunications Intersystem Operations (ANSI/TIA/EIA-41-D), which is herein referred to as ANSI-41, defines the messages and parameters that are used by a serving mobile switching center (MSC) to request authentication of a mobile system access from the MS's AC. A copy of ANSI-41 may be obtained via a world wide web site located at www.tiaonline.org, or by writing to Telecommunications Industry Association, 1300 Pennsylvania Ave., Suite 350, Washington, D.C. 20004 USA. The response to the authentication request may contain a parameter (Deny Access) indicating that the authentication failed and that access should be denied. Alternatively, the response may contain parameters requesting that additional authentication operations (e.g., unique challenge or SSD update) be performed. Currently, ANSI-41 does not allow the response to include both the Deny Access parameter and parameters requesting an authentication operation. Thus, if the AC requests that a subsequent authentication operation be performed following a GCA failure, the serving MSC will have no knowledge of the authentication failure. As a result, the serving MSC will be unable to decide based on the result of the GCA whether to perform the requested operation prior to or in parallel with call setup. This could lead to fraudulent system access or fraudulent subscriber feature profile updates. Thus there is a need for a method by which the MSC can decide when to initiate call setup based on knowledge of the GCA procedure results. FIG. 1 is a block diagram of a system that can be used to implement the method of reducing fraudulent system access of the present invention. FIG. 2 is a flow diagram of the preferred embodiment of the method of reducing fraudulent system access of the present invention The present invention provides a method by which call setup can be scheduled based on knowledge of the GCA procedure results. In the preferred embodiment, the method allows a serving MSC to make decisions regarding whether call setup should be delayed when performing an authentication operation on the traffic channel based on the outcome of GCA. FIG. 1 is a block diagram of a communication system 100 that can implement the preferred embodiment of the present invention. The system 100 includes a Mobile Switching Center/Visitor Location Register (MSC/VLR) 104 coupled between a Mobile Station (MS) 102 and a Home Location Register/Authentication Center (HLR/AC) 106. It should be recognized by one of ordinary skill in the art that the system 100 may include multiple MSs. The invention may be implemented in a system comprising any MS capable of authentication, a MSC/VLR model number EMX2500 or EMX5000 and a HLR/AC model number HLR41/AC. All three components are available from Motorola, Inc. The MSC/VLR 104 transmits a first message, preferably an ANSI-41 AuthenticationRequest INVOKE message 108, to the HLR/AC 106 to invoke GCA on the MSs. Upon receipt of the first message 108, the HLR/AC 106 processes the message according to authentication procedures defined in ANSI-41. The HLR/AC 106 sends a second message, preferably an ANSI-41 AuthenticationRequest RETURN RESULT (ARRR) message 110, to the MSC/VLR 104 informing the MSC/VLR 104 of the GCA result. In accordance with the preferred embodiment of the present invention, if the GCA fails, the ARRR message 110 includes a new parameter called AuthenticationFailureEvent (AFE), which contains the reason for the authentication failure. Additionally, if the HLR/AC 106 is provisioned to initiate a follow-up authentication operation, such as a unique challenge (authentication of a particular MS) or SSD update, the ARRR message 110 also includes the parameters necessary for the follow-up authentication operation. The MSC/VLR 104 communicates with the MS 102 through message 114 to request the follow-up authentication operations and through message 112 to receive the results. The addition of the AuthenticationFailureEvent parameter to the ARRR message 110 allows the MSC/VLR 104 to make decisions regarding whether call setup for the MS 102 should be delayed while performing the follow-up authentication operation on the traffic channel. This minimizes call setup delay while reducing the occurrence of fraudulent system access by the MS 102. FIG. 2 is a flow diagram of the preferred embodiment of the method of reducing fraudulent system access by a mobile station. As previously stated, when the MSC/VLR 104 desires to authenticate a MS on system access, it sends an AuthenticationRequest INVOKE message 108 to the HLR/AC 106 to invoke GCA authentication. Upon receiving the message 108, the HLR/AC 106 performs authentication processing according to known procedures. Upon completion, the HLR/AC 106 sends an ARRR message 110 to the MSC/VLR 104. Referring to FIG. 2, at step 202, the MSC/VLR 104 determines whether the Deny Access parameter is included in the ARRR message. If the parameter is not included, the MSC/VLR 104 determines whether parameters associated with other operations, such a unique challenge or SSD update, are included in the ARRR message (step 204). If such parameters are included, at step 206, the MSC/VLR 104 determines whether the criteria for invoking the operation are met. If the criteria are met, the MSC/VLR 104 determines whether the AuthenticationFailureEvent parameter is included in the ARRR message (step 208). If the parameter is included, the MSC/VLR 104 invokes the operation (step 218). At step 220, the MSC/VLR 104 determines whether the operation is successful. If the operation is successful, the MSC/VLR 104 initiates call setup (step 222). If the operation is not successful, the MSC/VLR 104 releases the call (step 224). Thus, by including both a notification that the initial authentication (GCA authentication) failed and parameters for a subsequent operation (e.g. further authentication) in the ARRR message, the MSC/VLR 104 is able to delay call setup for the MS 102 until determining whether the subsequent operation is successful. Referring back to step 208, if the AFE parameter is not included in the ARRR message, the MSC/VLR 104 initiates call set up (step 210) and then invokes the subsequent operation (step 212). At step 214, the MSC/VLR 104 determines whether the operation is successful. If the operation is successful, the MSC/VLR 104 continues with call set up (step 216). If the operation is not successful, the MSC/VLR 104 releases the call (step 224). Thus, if the MSC/VLR 104 receives parameters for a subsequent operation and GCA was successful, it does not delay call setup. Instead, the MSC/VLR 104 initiates call setup in parallel with initiating the subsequent operation. If the operation is successful, call setup is continued. If the operation is unsuccessful, the call is released (i.e., call setup is halted). Referring back to step 206, if the criteria for invoking the operation are not met, the MSC/VLR 104 informs the HLR/AC 106 that the operation cannot be performed (step 228). Referring back to step 204, if parameters associated with another operation are not included in the ARRR message, the MSC/VLR 104 initiates call setup (step 226). Thus, if GCA does not fail and additional authentication operations are not requested, the MSC/VLR 104 proceeds with call setup. Referring back to step 202, if the deny access parameter is included in the ARRR message, the MSC/VLR 104 releases the call (step 224). While the invention may be susceptible to various modifications and alternative forms, a specific embodiment has been shown by way of example in the drawings and has been described in detail herein. However, it should be understood that the invention is not intended to be limited to the particular forms disclosed. Rather, the invention is to cover all modification, equivalents and alternatives falling within the spirit and scope of the invention as defined by the following appended claims.
Windows firewall settings If Windows Firewall is enabled on the machine on which Service Virtualization is installed, requests from remote services to Service Virtualization are blocked. To enable the required TCP/HTTP communication, Service Virtualization adds a set of exceptions to the Firewall. This set of inbound rules is maintained automatically by Service Virtualization, and does not generally require any manual configuration. To change the automatic configuration settings, see Windows firewall and TCP port configuration. For TCP listeners, a firewall exception is created for the Service Virtualization Server and Designer executable files. For HTTP listeners, Service Virtualization uses the .NET HttpListener component to listen for HTTP/HTTPS requests. Service Virtualization cannot define an exception for the HttpListener executable itself, because HttpListener runs in a separate kernel process and is shared by all applications running on the machine. Instead, a firewall exception is created for all ports where the HttpListener component is used by the Service Virtualization Designer or Server to listen for HTTP/HTTPS requests. The Service Virtualization components use the listeners as follows: - SSL component of the HTTP Proxy agent The Service Virtualization installer creates a firewall exception for the Service Virtualization Server and Designer executables. - HTTP Gateway agent - HTTP port of the HTTP Proxy agent - JDBC agent - Service Virtualization Management API endpoint in unsecured mode - HTTPS Gateway agent - Service Virtualization Management API endpoint in secured mode Service Virtualization creates firewall exceptions for the specific ports that the agents use, makes the relevant URL reservations, and registers an SSL certificate for each port listening for HTTPS requests. Note: All firewall rules that Service Virtualization creates are removed if the product is uninstalled. The default inbound rules that Service Virtualization creates during installation of the Designer or when the Server is run for the first time are as follows: - Rules with specified ports are used by the System HTTP Listener server, and not directly by Service Virtualization. The ports are open for any program running on the machine. - Rules that are assigned directly to the Service Virtualization applications enable the Service Virtualization agents to access TCP ports directly. \|Service Virtualization Designer\|\|VirtualServiceDesigner\|\|Any\| \|Service Virtualization Designer (HTTP Gateway)\|\|Any\|\|7200\| \|Service Virtualization Designer (HTTP Proxy)\|\|Any\|\|7201\| \|Service Virtualization Designer (HTTPS Gateway)\|\|Any\|\|7205\| \|Service Virtualization Designer (Java SE 6/7 JDBC)\|\|Any\|\|7288\| \|Service Virtualization Designer \|Service Virtualization Server\|\|HP.SV.StandaloneServer\|\|Any\| \|Service Virtualization Server (HTTP Gateway)\|\|Any\|\|6070\| \|Service Virtualization Server (HTTP Proxy)\|\|Any\|\|6071\| \|Service Virtualization Server (HTTPS Gateway)\|\|Any\|\|6075\| \|Service Virtualization Server (Java SE 6/7 JDBC)\|\|Any\|\|6088\| Service Virtualization Server (RestManagementService) \|Service Virtualization Management (HTTP Server)\|\|Any\|\|6086\| To review the current Windows firewall settings for Service Virtualization: - In Windows Control Panel, open Windows Firewall. - Select Advanced Settings to open Windows Firewall with Advanced Security. Select Inbound Rules, and sort by group. The rules defined for Service Virtualization start with Service Virtualization Designer or Service Virtualization Server. All rules are created by Service Virtualization for the Windows Firewall Private profile, using TCP protocol, and are enabled by default.
\|XINETD.LOG(5)\|\|File Formats Manual\|\|XINETD.LOG(5)\| NAME¶xinetd.log - xinetd service log format DESCRIPTION¶A service configuration may specify various degrees of logging when attempts are made to access the service. When logging for a service is enabled, xinetd will generate one-line log entries which have the following format (all entries have a timestamp as a prefix): The data depends on the entry. Possible entry types include: - generated when a server is started - generated when a server exits - generated when it is not possible to start a server - generated if the USERID log option is used. - generated if the USERID log option is used, and the IDONLY service flag is used, and the remote end does not identify who is trying to access the service. In the following, the information enclosed in brackets appears if the appropriate log option is used. A START entry has the format: An EXIT entry has the format: type can be either status or signal. The number is either the exit status or the signal that caused process termination. A FAIL entry has the format: Possible reasons are: - a certain number of consecutive fork attempts failed (this number is a configurable parameter) - the time check failed - the address check failed - the allowed number of server instances for this service would be exceeded - a limit on the number of forked processes was specified and it would be exceeded A DATA entry has the format: The data logged depends on the service. - remote_user=%s local_user=%s tty=%s - remote_user=%s verify=status command=%s Possible status values: - the password was correct - the password was incorrect - no such user - remote_user=%s local_user=%s command=%s - received string or EMPTY-LINE A USERID entry has the format: The text is the response of the identification daemon at the remote end excluding the port numbers (which are included in the response). A NOID entry has the format: SEE ALSO¶xinetd(1L), xinetd.conf(5) \|28 April 1993\|
We propose a secure Internet Protocol version 6 (IPv6) address configuration scheme for a Mobile Ad Hoc Network. The scheme presents the architecture for a Mobile Ad Hoc Network and the hierarchical IPv6 address structure. In the architecture, a new node can acquire a unique IPv6 address from a proxy node within one-hop scope without performing a duplicate address detection process, and the IP addresses occupied by failed nodes can be recovered automatically for reuse. On the basis of the hierarchical IPv6 address structure, each proxy node can acquire a unique address scope for assignment and assign a unique IP address for new nodes, so the address configuration task is distributed around proxy nodes. In addition, the transmission scope of the control packets for address configuration is controlled within one-hop scope, so the address configuration cost is reduced, and the delay is shortened. The security of the proposed address configuration scheme is achieved through authentication. The paper analyzes the performance parameters of the proposed scheme and the existing schemes, and the analytical results show that the address configuration cost of the proposed scheme is lower and the delay is shorter.
Crooks pretend to be from Google and try to steal users credential data Not so long ago, cybersecurity researchers discovered a malware string which is spread by crooks who pretend to be from the well-known company Google. These criminals use fake Google reCAPTCHA to target users who use online banking services and steal important credential information from their accounts. According to a researcher from the Sucuri organization, a malicious attempt was made against Poland: During a recent investigation, we discovered a malicious file related to a phishing campaign that targeted a Polish bank. This campaign employed both the impersonation and panic/bait techniques within an email in order to lure victims into downloading banking malware. A 404 error page is provided once the user clicks on the malicious hyperlink Talking about the malicious spam emails, crooks try to make them look illegitimate so that users have no doubts and open them immediately. These messages are sent in a form of accepting a certain type of transaction. As additional content, a hyperlink is inserted inside the letter which the victim is supposed to access. However, once clicked, such link redirects the user to malware-laden payload which is inserted in a .PHP document. The most interesting part of this malicious attempt is that it operates a little bit differently than other phishing campaigns are used to. Once the malicious link is clicked, the victim receives a 404 error page instead of being redirected to a misleading and fake banking site where he/she is supposed to enter private details. Continuously, the Sucuri cybersecurity company claims that if other alternative search engines are being used, the malicious .PHP code displays a fake Google reCAPTCHA: By filling false Google reCAPTCHA, victims download malware to their Android devices The fake Google reCAPTCHA technique is used by criminals in order to successfully distribute malware. However, users need to know that the images which are loaded always appear to be the same due to technical purposes. What else makes the made-up reCAPTCHA different from the original version is that it cannot load audio files. If the victim manages to click on the CAPTCHA, fill in all required boxes and download the given payload, he/she installs the malware straight to the device by not even noticing that. Once the CAPTCHA is filled, users receive a malicious .zip file and .APK for their Android mobile phones or other devices. VirusTotal has already uploaded the examples of the spotted virus. This type of cyber threat can be detected as Banker Trojan, BankBot, Artemis, Evo-gen, and in various other names by different antivirus tools. Also, it is known that this trojan mostly targets Android users and is capable of gathering various data about the device and performing actions such as: - finding the location; - checking the state of the device; - sending text messages; - making calls,
Intrusion Prevention System (IPS) SearchSecurity, February 12th, 2020 February 12, 2020, Volume 263, Issue 2 An intrusion prevention system (IPS) is a network security and threat prevention tool "The idea behind intrusion prevention is to create a preemptive approach to network security so potential threats can be identified and responded to swiftly. Intrusion prevention systems are thereby used to examine network traffic flows in order to find malicious software and to prevent vulnerability exploits..." Read More ...
Successful firewall management The leading cause of data breaches and ransomware attacks are misconfigured firewalls. Recently, Gartner reported that misconfiguration causes 99% of firewall breaches. Firewalls are a powerful security product when configured correctly. However, there are many settings required to make them effectively stop threats; and the problem is that the firewall will be largely ineffective if not configured properly. As firewall rules are created, copied, and amended, they can actually be detrimental to each other due to unwanted performance and security consequences. Conflicting rules and misconfigurations may make the entire network vulnerable to complex threats, intrusion and unauthorized access. Rampart Gateway Control provides comprehensive security management through complete transparency into your network’s traffic and threats which builds a security defense template for standardizing the management and deployment of your business firewall. Our Security Operations Center (SOC) Team receives custom alert notifications in real-time when there are changes made to the firewall. The Power of Evidence & Logs How to Stop a Ransomware Attack If your business has just been attacked with ransomware, you’re probably wondering how this happened and you should have done to stop it. For many organizations, the answer isn’t completely clear. All too often, businesses have weaknesses in quite a few areas of their cybersecurity practices that pave the way for an attack. While most executives have security software implemented, they frequently overlook security solutions they should use themselves. Here’s 6 key things you should have done to stop ransomware:
MALM: Malware Monitor MALM is a Windows x86 and x64 compatible tool that records new processes, new modules loaded by existing processes, and new executable heaps in existing processes. Run this prior to running the malware sample. malm will log changes it has found, and upon closing (CTRL-C) this tool will print a final report of the state change from the beginning to the end. This tool is quite useful for monitoring where malware resides after execution. This tool is based upon snapshots,so it can miss processes, modules, or heaps that exist for only a short period of time. I am maintaining a public binary release download page for this project at: http://split-code.com/malm-malware-monitor.html The command-line flags for MALM are as follows: -q: quick mode. Only generates final report, instead of continually taking snapshots and printing the incremental reports. -t [seconds]: time limit. Quit and generate final report after the specified number of - Run cmd.exe as Administrator. - In cmd.exe, run MALM. - Execute the malware sample to monitor. - Wait for the malware sample to infect your system. MALM will be printing the incremental reports in cmd.exe. - In cmd.exe, press CTRL-C. The final report will be printed at this time. The following is a recording when running a live malware sample that allocates executable heaps in the existing svchost.exe process, and copies itself into it. The final report was triggered by a CTRL-C keyboard command. Take appropriate precautions when handling computer viruses - this tool simply monitors the system and does not prevent infection. PID 690,7.exe: New process. PID 690,7.exe: No longer accessible from current process security token. PID 690,7.exe: Terminated. PID 3DC,svchost.exe: New executable heap at 0x7A0000 PID 3DC,svchost.exe: New executable heap at 0x7A1000 PID 3DC,svchost.exe: New executable heap at 0x7A3000 PID 3DC,svchost.exe: New executable heap at 0x7A6000 PID 3DC,svchost.exe: New executable heap at 0x7A8000 PID 3DC,svchost.exe: New executable heap at 0x7B2000 PID 3DC,svchost.exe: New executable heap at 0x7B3000 PID 3DC,svchost.exe: New executable heap at 0x7B6000 Final report of final state versus starting state. --- PID 3DC,svchost.exe --- new exec heap: 7A0000 new exec heap: 7A1000 new exec heap: 7A3000 new exec heap: 7A6000 new exec heap: 7A8000 new exec heap: 7B2000 new exec heap: 7B3000 new exec heap: 7B6000 Contributions are welcome. Some possible contribution directions are as follows: - Upgrade the i_module comparer to return 'False' when the code at the module entry point has changed, or if the PE header has changed. Some malware gut out existing modules in memory and replace the code with their malware. In this scenario, this tool may not register the hidden location of the malware within the legitimate previously-loaded module. - Kernel address-space executable region monitoring. - Maybe add filesystem and registry change monitoring with a flag. - Anything else you can think of. Copyright 2012 Geoff McDonald, and other contributors. http://split-code.com/ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
The article discusses a recently patched vulnerability in a Google Fonts optimization plugin for WordPress, which was rated as High. The plugin, called OMGF \| GDPR/DSGVO Compliant, Faster Google Fonts. Easy., aims to optimize the use of Google Fonts to reduce page speed impact and ensure GDPR compliance for users in the European Union. The vulnerability allows unauthenticated attackers to delete entire directories and upload malicious scripts, posing a significant risk to the security of websites using the plugin. Specifically, the vulnerability enables unauthenticated directory deletion and the upload of Cross-Site Scripting (XSS) payloads. XSS is a type of attack where a malicious script is uploaded to a website server, allowing attackers to remotely attack the browsers of site visitors. The cause of the vulnerability, as identified by Wordfence researchers, is a lack of a capability check, which is a security feature that verifies whether a user has access to specific features within a plugin. The official WordPress developer page for plugin makers emphasizes the importance of capability checking to assign specific permissions to users or user roles, preventing unauthorized access to sensitive website functionalities. Wordfence also indicates that previous updates attempted to address the security gap, but version 5.7.10 is deemed the most secure version of the plugin. The vulnerability warning provided by Wordfence advises that versions up to and including 5.7.9 are vulnerable to unauthorized modification of data and stored Cross-Site Scripting. In conclusion, the vulnerability in the Google Fonts optimization plugin poses a serious threat to the security of up to 300,000 websites using the plugin. Users are advised to update to the latest, most secure version of the plugin (version 5.7.10) to mitigate the potential risks associated with the vulnerability. Additionally, plugin developers are urged to prioritize capability checks to prevent unauthorized access and modification of sensitive website features. Read Full Article
Malicious Packet Loss Identification in Disruption Tolerant NetworkAuthor : J. Shaldit Sheni, K.Jayashree and B.Fowzia Sihana Volume 3 No.1 January-June 2014 pp 36-40 In recent days network is suffering serious problems with the packet loss. Dropping of received packets, even it has adequate buffers is very common in Disruption Tolerant Network. The DTN node facilitates communication between mobile nodes. Sometimes the mobile node selects the DTN with lowest reputation that affects packet delivery ratio. If there is a malicious node in the route, the data packet does not reach its destination. Repeatedly the misbehaving nodes may forge some records to avoid being spotted. To solve these issues we propose a scheme to limit the packet rolling in the direction of misbehaving node. The contact record preserves the previous performance of DTN and the mobile nodes select the best rated DTN for its communication. The record handler is maintained to keep track of incoming and outgoing packets. The witness nodes identify the real misbehaving node. The malicious node needs to be identified and is barred. The genuine packet loss, malicious packet loss are differentiated. DTN, MN, Packet Loss, Misbehaving Node, Malicious Packet Loss, Packet life time
CACEE: Context Aware Concolic Execution Engine for Malware Analysis MetadataShow full item record An emerging pattern in malware is the use of public web services for command andcontrol (C&C) infrastructure. This new trend, combined with the short lifespan of malwarein the wild, makes extracting behaviors from malware in an automated fashion a difficultproblem. The Context-Aware Concolic Execution Engine (CACEE) is a tool designed torecreate the original execution context, forcing Windows 32-bit malware to execute theirpayloads as if they were still operational. CACEE monitors the flow of data as the payloadexecutes, and uses this information to synthesize the behaviors the malware exhibits. Threemalware case studies that abuse public web services are analyzed with CACEE, and theresults are compared against manual reverse engineering.
I just installed influxdb v1.7 on linux and I’m trying to understand it so basicly I’m a noob in the influx`s world. I understood that there are a web api that I can use to query but it seems that something is wrong. Whenever I try to query via http (and port 8086) on my pc I’m getting the 404 error page not found. I enabled http section: # Determines whether HTTP endpoint is enabled. # enabled = true # The bind address used by the HTTP service. # bind-address = ":8086" # Determines whether HTTP authentication is enabled. auth-enabled = true
Skip to Main Content Adopting publicly accessible platforms such as cloud computing model to host IT systems has become a leading trend. Although this helps to minimize cost and increase availability and reachability of applications, it has serious implications on applications' security. Hackers can easily exploit vulnerabilities in such publically accessible services. In addition to, 75% of the total reported application vulnerabilities are web application specific. Identifying such known vulnerabilities as well as newly discovered vulnerabilities is a key challenging security requirement. However, existing vulnerability analysis tools cover no more than 47% of the known vulnerabilities. We introduce a new solution that supports automated vulnerability analysis using formalized vulnerability signatures. Instead of depending on formal methods to locate vulnerability instances where analyzers have to be developed to locate specific vulnerabilities, our approach incorporates a formal vulnerability signature described using OCL. Using this formal signature, we perform program analysis of the target system to locate signature matches (i.e. signs of possible vulnerabilities). A newly-discovered vulnerability can be easily identified in a target program provided that a formal signature for it exists. We have developed a prototype static vulnerability analysis tool based on our formalized vulnerability signatures specification approach. We have validated our approach in capturing signatures of the OWSAP Top10 vulnerabilities and applied these signatures in analyzing a set of seven benchmark applications.
The central government is making moves to combat the rise in fraud calls from international numbers and fake caller IDs. In the fiscal year 2023-24, the government blocked 65 telecom setups that were enabling these deceptive practices, allowing calls with fake Indian numbers. “So far, 65 such illegal setups during FY 2023-24, 62 in FY 2022-23 and 35 in FY 2021-2022 have been unearthed,” the Minister of State for Communications, Devusinh Chauhan stated in a written reply in Lok Sabha. Don’t pick up Calls coming from these Number Codes According to TOI, the Department of Telecommunications (DoT) is taking charge by instructing International Long Distance Operators (ILDOs) to reject incoming calls lacking proper Caller Line Identification (CLI) or carrying certain prefixes. Specifically, calls with prefixes like +11, 011, 11, +911 to +915 are being targeted to prevent international calls from having faked Indian landline numbers. Fight against Illegal Setups Working closely with Law Enforcement Agencies and Telecom Service Providers (TSPs), the DoT is actively uncovering and dismantling these illegal telecom setups. These setups are often exploited for anti-national activities, cyber-crimes, and financial frauds. Security Measures and App Blocking To tackle the issue at its roots, the DoT is not only disconnecting reported mobile connections linked to such setups but also blocking apps that facilitate the generation of fraudulent calls. These apps are being barred from platforms like Google Play Store and Apple App Store. New SIM Guidelines The DoT has also implemented a set of security measures for purchasing new SIM cards, effective from December 1. These measures are aimed at curbing fraud related to SIM cards and enhancing overall security.
This workshop has been deprecated and archived. The new Amazon EKS Workshop is now available at www.eksworkshop.com. Security is a critical component of configuring and maintaining Kubernetes clusters and applications. Amazon EKS provides secure, managed Kubernetes clusters by default, but you still need to ensure that you configure the nodes and applications you run as part of the cluster to ensure a secure implementation. Since CIS Kubernetes Benchmark provides good practice guidance on security configurations for Kubernetes clusters, customers asked us for guidance on CIS Kubernetes Benchmark for Amazon EKS to meet their security and compliance requirements. In this chapter, we take a look at how to assess the Amazon EKS cluster nodes you have created against the CIS EKS Kubernetes benchmark.
Virtual Reality (VR) has received attention since the trend of the Metaverse came after the pandemic era. Several studies look into how Virtual Reality can be used in higher education. because all the research comes from the education field. However, previous research is too narrow in using technology, but not the area, impact, and challenges of Virtual Reality itself. Therefore, the systematic literature review (SLR) from 2016 to 2022 is discussed in this work to research how discipline, impact, and challenges of Virtual Reality in Higher Education. From 702 papers retrieved, and 214 candidates, to determine the applicability of the study question, 62 papers were chosen. The result shows that most discipline areas are still in computer science, followed by education and public health. Most reports said Virtual Reality is still highly impacted in solving the problem, especially in public health and education. However, there are still some pitfalls: the challenge of using The Head Mounted Device makes Virtual Reality-Induced symptoms and effects (VRISEs) such as Cyber sickness, simulator sickness, Motion sickness, dizziness, and physical discomfort. The implication of the SLR will be considered for academic purposes in preparing for the Metaverse era, especially in higher education.
A Macro Virus is a type of malicious code that attaches itself to documents and uses the macro programming capabilities of the document’s application to execute, replicate, and spread or propagate itself. Source: CNSSI 4009 Please log in again. The login page will open in a new tab. After logging in you can close it and return to this page.
\|Title\|\|A Framework for Secure Execution of Software\| \|Publication Type\|\|Journal Article\| \|Year of Publication\|\|2004\| \|Authors\|\|A. Mana, J. Lopez, J. J. Ortega, E. Pimentel, and J. M. Troya\| \|Journal\|\|International Journal of Information Security (IJIS)\| The protection of software applications is one of the most important problems to solve in information security because it has a crucial effect on other security issues.We can find in the literature many research initiatives that have tried to solve this problem, many of them based on the use of tamperproof hardware tokens. This type of solutions depends on two basic premises: (i) to increase the physical security by using tamperproof devices, and (ii) to increase the complexity of the analysis of the software. The first premise is reasonable. The second one is certainly related to the first one. In fact, its main goal is that the pirate user can not modify the software to bypass an operation that is crucial: checking the presence of the token. However, the experience shows that the second premise is not realistic because the analysis of the executable code is always possible. Moreover, the techniques used to obstruct the analysis process are not enough to discourage an attacker with average resources. In this paper, we review the most relevant works related to software protection, present a taxonomy of those works and, most important, we introduce a new and robust software protection scheme. This solution, called SmartProt, is based on the use of smart cards and cryptographic techniques, and its security relies only on the first of previous premises; that is, Smartprot has been designed to avoid attacks based on code analysis and software modification. The entire system is described following a lifecycle approach, explaining in detail the card setup, production, authorization, and execution phases. We also present some interesting applications of Smart- Prot as well as the protocols developed to manage licenses. Finally, we provide an analysis of its implementation details. A Framework for Secure Execution of Software
Cisco Talos have discovered that attackers leveraged a malicious Cisco job themed document based off of content from a legitimate Cisco job posting. A Microsoft Word document containing malicious macros was used to drop malware. The macros extract an encoded executable, drop it onto the file system, and execute it. Upon execution, the binary establishes a connection to a C2 server in order to receive an additional payload. At the time of analysis, the second-stage payload was no longer available so the exact purpose of the final payload is unable to be determined. Cisco Talos researchers identified malware samples from 2017 that appear to be connected to this campaign. These additional samples were job-themed Word documents, used very similar encoding and obfuscation techniques and performed the same functions. Cisco Talos believes that this campaign and the campaigns associated with the previous samples are likely connected to the same threat actor. Additionally, they note that the TTPs seen in the campaigns indicate a sophisticated attacker. Indicators of Compromise Duncan is a technology professional with over 20 years experience of working in various IT roles. He also has a wide range of other skills in radio, electronics and telecommunications.
Create additional rules 1 min read With your first rule in place, you can then create a broader set of rules to protect your application. If your application is a bit more complex - for example, receiving mobile application traffic and automated API traffic - you may need to layer your bot protection rules for the best results. The following two rules might be useful for a site protecting against content scraping, or some other form of bots viewing resources intended for humans. Since the order of the rules matters for rule execution, you should always place your allow rules before block or challenge rules. Rule 1 - Allow mobile app request Rule 2 - Restrict automated traffic, but exclude /api path Protect specific endpoints If bots are submitting data through your forms, your rules may be more focused on protecting specific, more vulnerable endpoints. Unit 6 of 7
n. A computer-generated animation based on the forensic evidence presented in a court case. What the jury saw is known as forensic animation — the computerized illustration of events recounted by courtroom testimony (in this case, the coroner's report and the state trooper's on-scene analysis). It's the newest in a chain of technologies — from lie-detector tests to handwriting analysis and DNA sampling — that is transforming the world of litigation. Each seminar reviews a variety of animation sequences used in trials, how-to tips and a demonstration of Autodesk 3D Studio software. The lecturers will address the benefits of forensic animation in a variety of court cases — criminal, personal injury, contraband, medical malpractice, product liability, air crashes and intellectual property.
AccessData announced the initial release of its new integrated security framework, CIRT (Cyber Intelligence and Response Technology), and is inviting select government and commercial clients to test drive the solution and provide feedback. CIRT is the first solution to integrate network forensics, computer forensics and large-scale data auditing into a single interface. Designed for security and response teams, as well as information assurance teams, CIRT allows personnel to analyze what is happening across the enterprise from multiple vantage points. CIRT enables cyber security personnel to proactively and reactively detect, analyze and remediate security threats in the most efficient manner by correlating network and host data within a single interface. Furthermore, it enables large-scale auditing and the correlation of network and host data, allowing organizations to quickly chase down and remediate classified spillage and files with embedded malware. Currently, organizations must rely on a variety of disparate tools to respond to incidents, such as advanced persistent threats and classified data leakage. For example, in the case of an advanced persistent threat, the organization may or may not be alerted to the threat by its alerting technologies. However, if an alerting system catches the incident, the organization must then chase down the malware by investigating both network traffic and data on individual computers across the enterprise. That traditionally requires the use of two or more different tools that do not integrate with each other. This lack of integration makes it extremely difficult to correlate the information and perform root cause analysis, in turn, making it next to impossible to identify all affected computers and thoroughly remediate the threat. With regard to information assurance, data spillage is often discovered by accident, or in the case of Wikileaks, it is discovered after the classified information has found its way onto a website. Ideally, organizations should conduct regular automated audits of the enterprise to identify whether any documents or emails containing sensitive information have spilled onto an unsecure segment of the network. Once sensitive content is discovered in unsecure locations, it is critical for an organization to be able to quickly determine root cause, how the spill propagated and how that content is leaving the organization’s enterprise. With an integrated view into computer data and network traffic, an information assurance team can more quickly determine whether a data leakage incident was a hack, an internal employee’s error or a malicious employee’s pet project.
Today’s threat actors are more advanced and well-funded than ever before, obfuscating their activities and identities to remain virtually anonymous. We are, however, able to use personal data attributes – IP addresses, crypto wallets, usernames and passwords, and similar identifiers – to track the breadcrumb trails between the point of compromise and the threat actor in the wake of an attack. Investigators can leverage breached data to piece together a bad actor’s digital footprint and track them down. With hundreds, if not thousands, of cybersecurity companies offering a wide variety of services, you would think we would be equipped to prevent nearly all cyber-attacks – or at the very least reduce the vast amount of financial and reputational damage that occurs each year. The technology that exists is far more sophisticated now than it was just five years ago, and it continues to evolve to keep pace with persistent cybercriminals. The problem is that the biggest cyber threat to an organization is its own people: human error. Your organization could leverage the best cyber solutions on the market, but poor cyber hygiene still runs rampant despite the industry’s repeated attempts to warn against password reuse, phishing attacks, and poorly configured or unprotected servers. To mitigate crises, enterprises can implement draconian measures – restricting access to social networking sites, whitelisting certain apps – or take an Orwellian route to the entire infrastructure and monitor the actions of every employee within its network. However, both approaches have major drawbacks. Instead of focusing on what is happening within an organization, it is time for organizations to anticipate and defeat external threats, which can be accomplished with identity attribution. Enterprises would be wise to monitor open sources on the surface, social, deep and dark web for exposed identity information, which can help security teams understand employee and executive digital footprints, as well as aid in developing an understanding of an enterprise’s adversaries. Attribution for disruption is beneficial to individuals and organizations on the frontlines of cyberwar – intelligence analysts, threat hunters, criminal investigators, financial and healthcare organizations, government and other public agencies, and many more. At Constella Intelligence, we have designed a powerful platform, Hunter, to improve the fraud investigation process to make it easier and quicker for investigators to identify malicious activity and attribute that activity to real-world identities. With Hunter, investigators are able to analyze data from multiple sources in one location, collate and compare across sources and identify connections and networks of activity. Understanding there is a real person behind the attack, security operation leaders must adapt and take a more proactive approach. By unmasking cybercriminals attacking your organization, you can take action to know your adversary and disrupt future attacks. Learn more about Hunter to see how it helps users efficiently attribute identities and identify further intelligence across multiple data sources simultaneously to expose the true identity of threat actors. The post Disrupt Threat Actors’ Malicious Activity with Identity Attribution appeared first on Constella. *** This is a Security Bloggers Network syndicated blog from Constella authored by Jonathan Nelson. Read the original post at: https://constellaintelligence.com/disrupt-threat-actors-malicious-activity-with-identity-attribution/
By the end of 2017, some 61% of businesses had implemented artificial intelligence (AI) into their organizations—a 23% jump from the previous year, according to Narrative Science. And the incorporation of AI into business will only rise: The number of medium and large enterprises using machine learning is predicted to double by the end of 2018, said Deloitte. Machine learning is a form of AI that interprets massive amounts of data, applying algorithms to the material, and making predictions off its observations. Common technologies that employ machine learning include facial recognition, speech recognition, translation services, and object recognition. SEE: Artificial intelligence: Trends, obstacles, and potential wins (Tech Pro Research) Businesses typically use machine learning for locating and processing large data sets that no human could sort through in a timely manner, if at all. Major companies like Amazon, IBM, Google, and Microsoft use machine learning to improve business functionality. But some organizations are implementing machine learning for more a narrow purpose: Cybersecurity. While many assume machine learning makes cybersecurity professionals’ lives much easier by better tracking security issues, that’s not necessarily the case. Just like any new technology, machine learning still has its flaws—problems that turn the tech into more of a headache than a helping hand in the security space. Here are the five ways machine learning may make things harder on cybersecurity pros. 1. Machine learning-equipped hackers Machine learning can be helpful defending against attackers, but can be destructive when used by the wrong people. “An arms race is occurring as each side tries to one-up the other to make a better AI,” said Ryan Ries, AI/machine learning expert at Onica. Machine learning works faster than humans—a quality that is typically celebrated. However, not in the case of cyberattacking efforts. “Human attackers will perform reconnaissance on a potential victim before launching a cyber attack, investigating things like what software they are running, the version of that software, any known vulnerabilities for said version, or any un-published zero day exploits shared among the hacker community that could improve their attack. This process can take many hours,” said Emil Hozan, security analyst at WatchGuard Technologies. “But with machine learning, this research process can be carried out much more quickly and efficiently. Machine learning/AI hacking can also learn from past experiences; what didn’t work on a similar previous hack attempt could be skipped over in favor of a new tactic.” 2. Lack of transparency In most cybersecurity systems, when a flaw is detected, the administrator can go in and see what caused the alert, according to Gartner research vice president Anton Chuvakin. However, with machine learning-based systems, the cause of alerts cannot be pinpointed, presenting a lack of transparency. Sometimes, these alerts end up being false positives, said Chuvakin. “Not only it can be wrong, but it’s also harder to ascertain, and, as people say in security, harder to triage what it means,” said Chuvakin. “Are we in real trouble? Are we in, somewhat of a trouble or are we not in trouble at all?” 3. Feeding the right data Machine learning systems don’t work when just any and all data are fed to it. These systems are actually a little picky. Modern machine learning algorithms rely on very specific data to work, said Chuvakin. “When we spoke to some of the vendors, they told us that the challenges are often not about machine learnings, but more about how you feed in the right data,” said Chuvakin. SEE: Enterprise IoT research: Uses, strategy, and security (Tech Pro Research) If companies want a quality output, the input has to be quality as well. “I would say that use of machine learning puts higher pressure on security professionals to deliver better quality input data, better quality sensor data,” Chuvakin said. “The old-school way the system may be less sensitive to quality inputs.” 4. Humans still need to make the system work Since machine learning systems can’t explain why something was flagged, something (or somebody) else is needed. Many people worry that AI will take jobs, but with the specialized skills needed for machine learning to work, more jobs might actually be created, said Chuvakin. “For the system to work, you have to have a security data scientist, which is obviously really rare and really expensive. It’s just a peculiar consequence of some of the advanced math being used in the product,” said Chuvakin. “Not only are the systems not always explainable, but to actually tune the product to operate effectively, you have to have skills that most security operations teams don’t have.” It doesn’t appear that machine learning is going to be replacing security professionals; most companies will actually need additional security pros to make the systems function properly. 5. The tech talent shortage The specialized skills necessary to make machine learning work creates another problem: need for hard-to-find talent. Talent shortages in the tech world, especially among data scientists, are definitely no secret. Handling machine learning systems is difficult, so it’s hard to find individuals able to help out with such a niche operating systems, leaving cybersecurity pros in a bind. “Machine learning is actually dramatically more difficult than most people realize,” said Chuvain. “Companies are having trouble finding talent. But think about it: If you have a large company and they want to use machine learning for business and they’re having trouble hiring, do you think the security team that doesn’t make money will be able to hire the right talent to do machine learning? The answer is very often no.”
Access control deals with preventing unauthorized operations on the managed data. Access control is usually performed against a set of authorizations stated by Security Administrators (SAs) or users according to the access control policies of the organization. Authorizations are then processed by the access control mechanism (or reference monitor) to decide whether each access request can be authorized or should be denied. Access control models for DBMSs have been greatly influenced by the models developed for the protection of operating system resources. For instance, the model proposed by Lampson is also known as the access matrix model since authorizations are represented as a matrix. However, much of the early work on database protection was on inference control in statistical databases. Then, in the 1970s, as research in relational databases began, attention was directed towards access control issues. As... - 1.Air Force Studies Board, Committee on Multilevel Data Management Security. Multilevel data management security. National Research Council, 1983.Google Scholar - 2.Berners-Lee T. et al. The semantic web. Scientific American, 2001.Google Scholar - 5.Carminati B., Ferrari E., and Perego A. Enforcing access control in web-based social networks. ACM trans. Inf. Syst. Secur., to appear.Google Scholar - 6.Carminati B., Ferrari E., and Tan K.L. A framework to enforce access control over Data Streams. ACM Trans. Inf. Syst. Secur., to appear.Google Scholar - 7.Carminati B., Ferrari E., and Thuraisingham B.M. Access control for web data: models and policy languages. Ann. Telecomm., 61(3–4):245–266, 2006.Google Scholar - 8.Carminati B., Ferrari E., and Bertino E. Securing XML data in third party distribution systems. In Proc. of the ACM Fourteenth Conference on Information and Knowledge Management, 2005.Google Scholar - 9.Castano S., Fugini M.G., Martella G., and Samarati P. Database security. Addison Wesley, 1995.Google Scholar - 10.Damiani M.L. and Bertino E. Access control systems for geo-spatial data and applications. In Modelling and management of geographical data over distributed architectures, A. Belussi, B. Catania, E. Clementini, E. Ferrari (eds.). Springer, 2007.Google Scholar - 13.Ferrari E. and Thuraisingham B.M. Security and privacy for web databases and services. In Advances in Database Technology, Proc. 9th Int. Conf. on Extending Database Technology, 2004, pp. 17–28.Google Scholar - 14.Ferrari E. and Thuraisingham B.M. Secure database systems. In O. Diaz, M. Piattini (eds.). Advanced databases: technology and design. Artech House, 2000.Google Scholar
Challenges of Hidden Data in the Unused Area Two within Executable Files A. W. Naji, A. A. Zaidan and B. B. Zaidan DOI : 10.3844/jcssp.2009.890.897 Journal of Computer Science Volume 5, Issue 11 Problem statement: The executable files are one of the most important files in operating systems and in most systems designed by developers (programmers/software engineers), and then hiding information in these file is the basic goal for this study, because most users of any system cannot alter or modify the content of these files. There are many challenges of hidden data in the unused area two within executable files, which is dependencies of the size of the cover file with the size of hidden information, differences of the size of file before and after the hiding process, availability of the cover file after the hiding process to perform normally and detection by antivirus software as a result of changes made to the file. Approach: The system designed to accommodate the release mechanism that consists of two functions; first is the hiding of the information in the unused area 2 of PE-file (exe.file), through the execution of four process (specify the cover file, specify the information file, encryption of the information, and hiding the information) and the second function is the extraction of the hiding information through three process (specify the steno file, extract the information, and decryption of the information). Results: The programs were coded in Java computer language and implemented on Pentium PC. The designed algorithms were intended to help in proposed system aim to hide and retract information (data file) with in unused area 2 of any execution file (exe.file). Conclusion: Features of the short-term responses were simulated that the size of the hidden data does depend on the size of the unused area2 within cover file which is equal 20% from the size of exe.file before hiding process, most antivirus systems do not allow direct write in executable file, so the approach of the proposed system is to prevent the hidden information to observation of these systems and the exe.file still function as usual after the hiding process. © 2009 A. W. Naji, A. A. Zaidan and B. B. Zaidan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Authenticating to remote services with only a password is a thing of the past. Modern attack techniques make theft and reuse of passwords simple, yet we continue to use them to secure pretty much everything. In this post, we will review the various risks associated with password authentication and discuss what can be done to improve our security posture. Determined human adversaries, or DHA for short, have changed the information security game for everyone. Many customers take actions in attempt to evict an emplaced attacker – actions that result in alerting the attacker to the organization’s knowledge of their presence, but don’t truly evict the attacker from the network. In this blog, we will … Continue reading “But I Reset the Password” – Remediating an Enterprise After a Targeted Attack The Cold War was a unique period in history; a period of high political tension lasting for almost 45 years whereby the world was divided into distinct categories of extremely capable countries. The term “Cold War” was coined by George Orwell in an article entitled “You and the Atomic Bomb” published in the Tribune on … Continue reading Cyber Warfare and the New Cold War
Mandiant has identified new malware that targets VMware ESXi, Linux vCenter servers, and Windows virtual machines. Novel malware discovered targeting VMware SXi hypervisors. Mandiant has released two blog posts detailing novel malware that targets VMware ESXi, Linux vCenter servers, and Windows virtual machines. In the first blog, researchers say that the malware ecosystem enables threat actors to do the following: - “Maintain persistent administrative access to the hypervisor; - “Send commands to the hypervisor that will be routed to the guest VM for execution; - “Transfer files between the ESXi hypervisor and guest machines running beneath it; - “Tamper with logging services on the hypervisor; - “Execute arbitrary commands from one guest VM to another guest VM running on the same hypervisor.” Mandiant identified a novel technique in which threat actors leveraged malicious vSphere Installation Bundles (VIBs) to install backdoors they’ve called “VIRTUALPITA” and “VIRTUALPIE” on the ESXi hypervisors. Mandiant notes that the threat actors need admin-level privileges to the ESXi hypervisor before malware can be deployed, and said that this is not an external remote code execution vulnerability, and that there is no evidence of a zero-day vulnerability that gives the malicious actors access. Hardening the hypervisor. The second blog details other attacker actions, describes ESXi detection methodologies, and discusses how to further harden hypervisors. Mandiant has attributed this malware to UNC3886, suspecting that the motivation is cyber espionage, with a possible connection to China. “While we are aware of less than 10 organizations where this malware was deployed, we anticipate more organizations will discover compromised VMware infrastructure in their environments as a result of this published threat intelligence. Most organizations do not have an efficient way to hunt for and identify threats on VMware hypervisors given the lack of EDR support. This is why Mandiant and VMware have collaborated and provided hardening guidance to organizations. It is critical for organizations to address this threat, as we anticipate other threat actors will develop similar malware capabilities over time,” said Mandiant Consulting CTO, Charles Carmakal. VMware has also released guidance following the discovery of the malware. Manish Gaur, head of product security at VMware, said, “VMware worked closely with Mandiant to understand this specialized malware so we could quickly arm our customers with the guidance they need to secure their vSphere environments and mitigate. While there is no VMware vulnerability involved, we are highlighting the need for strong Operational Security practices that include secure credential management and network security, in addition to following VMware’s hardening guidelines for virtual infrastructure.”
In May, cybersecurity researchers at Wordfence Threat Intelligence discovered a critical Authentication Bypass vulnerability in the WordPress Social Login and Register Plugin developed by miniOrange, allowing attackers to access user accounts via the associated email address. Upon notifying miniOrange, the company released a patch, version 7.6.4, on June 12. However, the patch wasn’t much effective as it still contained a vulnerability. A fully patched version, 7.6.5, was subsequently released on June 14. The flaw potentially affects over 30,000 WordPress websites. All users using the WordPress Social Login and Register plugin are requested to update their website to version 7.6.5 immediately to ensure they are protected against this security risk. The vulnerability in the WordPress Social Login and Register plugin arises from insufficient encryption during the login process. Specifically, the plugin does not adequately encrypt the user data supplied during login validation, allowing unauthenticated attackers to log in as any existing user on the site, including administrators if they possess the associated email address. While the initial patch addresses some aspects of the vulnerability, it was only fully resolved in version 7.6.5. Detailed technical analysis revealed that the encryption key used in vulnerable plugin versions was hardcoded, not unique paper WordPress installation. This critical oversight enabled threat actors to create valid requests containing properly encrypted email addresses, bypassing authentication and gaining access to arbitrary user accounts. Exploiting authentication bypass vulnerabilities can lead to the complete compromise of the WordPress site and further malicious activities. The disclosure timeline indicates that Wordfence engaged with miniOrange and provided full vulnerability details. The security company, although delayed, released firewall rules to protect its premium users against potential exploits. The delay in releasing the firewall rule for free users was to prevent disruption of the plugin’s core functionality.
What is VirTool:MSIL/Aikaantivm.GG!MTB infection? In this article you will locate concerning the meaning of VirTool:MSIL/Aikaantivm.GG!MTB as well as its negative influence on your computer. Such ransomware are a kind of malware that is clarified by online scams to require paying the ransom money by a target. In the majority of the situations, VirTool:MSIL/Aikaantivm.GG!MTB virus will certainly advise its targets to initiate funds transfer for the purpose of reducing the effects of the changes that the Trojan infection has introduced to the target’s gadget. These adjustments can be as follows: - Network activity detected but not expressed in API logs; - Ciphering the papers located on the sufferer’s hard disk drive — so the victim can no longer make use of the data; - Preventing routine accessibility to the sufferer’s workstation; The most typical networks through which VirTool:MSIL/Aikaantivm.GG!MTB Trojans are injected are: - By means of phishing e-mails; - As an effect of customer ending up on a source that organizes a harmful software program; As soon as the Trojan is successfully infused, it will either cipher the data on the victim’s PC or prevent the device from functioning in a proper fashion – while likewise placing a ransom money note that discusses the requirement for the sufferers to impact the repayment for the function of decrypting the papers or bring back the data system back to the first problem. In many instances, the ransom note will certainly come up when the customer reboots the COMPUTER after the system has actually already been harmed. VirTool:MSIL/Aikaantivm.GG!MTB distribution channels. In various edges of the globe, VirTool:MSIL/Aikaantivm.GG!MTB grows by leaps and also bounds. However, the ransom money notes and also techniques of extorting the ransom quantity may vary depending upon specific neighborhood (local) settings. The ransom money notes and also tricks of extorting the ransom quantity may differ depending on particular local (regional) settings. Faulty alerts concerning unlicensed software application. In specific locations, the Trojans typically wrongfully report having actually found some unlicensed applications enabled on the target’s device. The sharp after that demands the individual to pay the ransom. Faulty declarations about unlawful material. In nations where software piracy is much less popular, this method is not as reliable for the cyber frauds. Additionally, the VirTool:MSIL/Aikaantivm.GG!MTB popup alert might falsely declare to be stemming from a law enforcement establishment and will report having located kid pornography or other illegal data on the gadget. VirTool:MSIL/Aikaantivm.GG!MTB popup alert may wrongly claim to be obtaining from a legislation enforcement institution as well as will report having located kid pornography or other unlawful data on the tool. The alert will in a similar way have a demand for the user to pay the ransom money. File Info:crc32: EFA45671md5: bf97f1dcf3b0f3dcedb078aa16535e45name: BF97F1DCF3B0F3DCEDB078AA16535E45.mlwsha1: f7148f3d8c92461eb94e5c5748abbdbd78cb5fe7sha256: d4b53cb2ea851128d50d3f4b762f37c90d416ab29395d23a9f91daa9dc242265sha512: bd139f68520c9040601c752c6f043b67bfd8a3ab7c352bef3b8689d0f511edb82e0c686be9a7b5f512d7617798f3325750d4a35371ad119ec548bf4196e47ff8ssdeep: 3072:KulHfdnNBwyo2Q2bg+bIcVoiWbIb0UQ6DKl6PSyYuZ:K6fdnNiyoIIPiWUbZFtype: PE32 executable (GUI) Intel 80386 Mono/.Net assembly, for MS Windows Version Info:Translation: 0x0000 0x04b0LegalCopyright: Copyright xa9 2020Assembly Version: 126.96.36.199InternalName: Update.exeFileVersion: 188.8.131.52CompanyName: LegalTrademarks: Comments: ProductName: UpdateProductVersion: 184.108.40.206FileDescription: UpdateOriginalFilename: Update.exe VirTool:MSIL/Aikaantivm.GG!MTB also known as: \|Elastic\|\|malicious (high confidence)\| \|Sophos\|\|ML/PE-A + Mal/MSIL-AX\| \|MAX\|\|malware (ai score=84)\| \|Cynet\|\|Malicious (score: 100)\| \|ESET-NOD32\|\|a variant of MSIL/PSW.Agent.RXP\| \|SentinelOne\|\|Static AI – Malicious PE\| How to remove VirTool:MSIL/Aikaantivm.GG!MTB ransomware? 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 VirTool:MSIL/Aikaantivm.GG!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 VirTool:MSIL/Aikaantivm.GG!MTB you can always ask me in the comments for getting help. User Review( votes)
- Trending Categories - Data Structure - Operating System - MS Excel - C Programming - Social Studies - Fashion Studies - Legal Studies - Selected Reading - UPSC IAS Exams Notes - Developer's Best Practices - Questions and Answers - Effective Resume Writing - HR Interview Questions - Computer Glossary - Who is Who Types of VoIP hacking and counter measures Voice over internet protocol is related to the collection of different technology that allows the easy and effective delivery of voice communication, video, audio, and images with the help of the data network through internet protocol. It can also be referred to as a technology that makes it very easier to make voice calls by making use of Internet connections. VoIP makes it very easier and more flexible to make calls as compared to the traditional telephone system. To reduce voice over Internet protocol hacking it is important to incorporate various measures that could include designing a strong password for the website or the file so that the files are protected from malicious users. VoIP hacking is concerned with making use of unauthorized access to the phone system to steal data. The hackers try to listen to the calls and steal critical information. The hacking is also done to make calls and increase the bills of the international communicator. These types of attacks usually occur in a situation when an insider or a known person will serve important information. These types of attacks could result in the use of customer information which would include the use of credit card information and using this information to gain money. Various types of VoIP hacking 1. Unauthorized use It is a type of attack vendor user or the hacker make use of the information of the phone network to make calls to other person or organization pretending someone is from the organization. These types of hackers or malicious users make use of the autodial link and robot calling software to connect to the phone network system. When the receiver takes the call a prerecorded message please could ask the listener to enter their information such as credit card details or bank details. 2. Toll fraud It is referred to as making international calls to other people and organizations that could result in increasing the bill amount and often resulting in major damage to the organization. It occurs when the attacker listens to certain business goals and communications without the user’s information. This type of malicious activity occurs when the date of information is shared through an unencrypted or unsecured channel of communication. 4. Call tampering It is one of the techniques used by the malicious user to create a disturbance in communication it could include injecting some noise packets to reduce the quality of communication. 5. DoS attack It is a type of attack that includes making a particular service unavailable by injecting huge traffic from multiple ends to the system. 6. Buffer overflow attacks It is a place for storing data temporarily. When more data is placed on a program or a system this results in the situation of data overflow. This could also result to the leaking of certain important data into other buffers that could also corrupt the owner’s holding. In the situation of a buffer overflow attack, the extra information can reach malicious users and they can change the data and damage the files. 7. Viruses and malware A virus is a court embedded that results to affecting the original information. The viruses are self-replicating and are being designed to hurt the program and the software. Malware is software that enters into the system without the owner’s consent to steal private and confidential information. Countermeasures for dealing with VoIP are as follows − Access to the website or the content should be implemented in a careful and controlled manner so that unauthorized access to the information can be reduced. Choosing a trusted voice over Internet protocol is very important so that the information is safe. Detailed analysis and network tests should be done periodically so that the problems in the network can easily be identified. Regular checking of the call logs and the history is equally important as it will help in identifying any problem regarding the use of data. The Password set up for the website needs to be strong enough so that hackers cannot simply crack it. The company needs to concentrate on adding more security checks and train software handlers to look out for any unwanted behavior. Voice over Internet protocol is a technology that is used to transport information and for communication. This could include making and allowing voice communications so that the critical information can easily be communicated. The other measures could include providing restricted control over the important information so that unauthorized use can be reduced. Kickstart Your Career Get certified by completing the courseGet Started
-- Corsaire Security Advisory -- Title: Symantec Enterprise Firewall (SEF) HTTP URL pattern evasion issue Date: 24.02.03 Application: Symantec Enterprise Firewall (SEF) 7.0 Environment: Windows NT 4.0, Windows 2000, Author: Martin O'Neal [[email protected]] Audience: General Distribution -- Scope -- The aim of this document is to clearly define some issues related to a URL pattern evasion issue in the HTTP proxy of the Symantec Enterprise Firewall (SEF) product, as supplied by Symantec Inc. -- History -- Vendor notified: 24.02.03 Document released: 26.03.03 -- Overview -- The SEF firewall product uses an application proxy strategy to provide enhanced security features for a variety of common protocols. For the HTTP proxy, part of this additional functionality allows the firewall to block URLs based on predefined regular expression patterns. However, by using URL encoding techniques this pattern matching functionality can be evaded. -- Analysis -- The HTTP pattern matching functionality works by analysing the HTTP URL format and comparing this against a database of predefined signatures. When an HTTP connection is processed via a rule that is configured to use the pattern matching functionality, it is checked against the signature database and if a match is found, the request is blocked with a 403 Forbidden error. However, if one of the standard URL encoding techniques (e.g. escaped encoding, Unicode, UTF-8) is used, then the pattern matching will fail to trigger and the attack will succeed. -- Proof of concept -- Step 1: On the firewall host create a rule that allows HTTP traffic and under the Advanced Services tab include the http.urlpattern setting. Step 2: Using the Editor open the httpurlpattern.cf file and add in a new line consisting of only the word "hamster". Save and reconfigure the firewall. Step 3: To reproduce this issue, open a standard web browser and connect to a site that will be included within the scope of the rule created in the first step (i.e. http://www.gerbil.com). This should result in a successful connection. Step 4: If the target pattern created in step 2 is appended to the same URL (i.e. http://www.gerbil.com/hamster) then the connection should fail with a 403 Forbidden error. Step 5: If a form of URL encoding is now used on the URL from step 4, (i.e. http://www.gerbil.com/h%69mster) then this will pass through the firewall successfully. -- Recommendations -- As an interim measure, the documentation that is supplied with the firewall should be revised to state explicitly that the pattern matching functionality does not support any form of underlying HTTP encoding schemes. Ideally, as a longer term solution the HTTP proxy should be enhanced so that encoding schemes are resolved and applied prior to performing the pattern matching function. Symantec have provided a knowledge base article for customers who wish to restrict all escaped character sequences in protected URLS, using a regular expression pattern . -- CVE -- The Common Vulnerabilities and Exposures (CVE) project has assigned the name CAN-2003-0106 to this issue. This is a candidate for inclusion in the CVE list (http://cve.mitre.org), which standardizes names for security problems. -- References -- http://enterprisesecurity.symantec.com/products/products.cfm?Pro ductID=47&EID=0 http://service1.symantec.com/SUPPORT/ent-gate.nsf/docid/20030325 07434754 -- Revision -- a. Initial release. b. Minor revisions. c. Minor revisions. d. Revised to include CVE reference. e. Revised to include Symantec recommendation. -- Distribution -- This security advisory may be freely distributed, provided that it remains unaltered and in its original form. -- Disclaimer -- The information contained within this advisory is supplied "as-is" with no warranties or guarantees of fitness of use or otherwise. Corsaire accepts no responsibility for any damage caused by the use or misuse of this information. Copyright 2003 Corsaire Limited. All rights reserved. CONFIDENTIALITY: This e-mail and any files transmitted with it are confidential and intended solely for the use of the recipient(s) only. Any review, retransmission, dissemination or other use of, or taking any action in reliance upon this information by persons or entities other than the intended recipient(s) is prohibited. If you have received this e-mail in error please notify the sender immediately and destroy the material whether stored on a computer or otherwise. DISCLAIMER: Any views or opinions presented within this e-mail are solely those of the author and do not necessarily represent those of Corsaire Limited, unless otherwise specifically stated. Corsaire Limited, 3 Tannery House, Tannery Lane, Send, Surrey, GU23 7EF Telephone: +44(0)1483-226000 Email:[email protected] oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo Delivery co-sponsored by TruSecure Corporation oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo TICSA - Anniversary Special - Limited Time Become TICSA certified for just $221.25 US when you register before 3/31/03 with PROMO "TS0103" at www.2test.com. NO membership fees, certification good for 2 years. Price for international delivery just $296.25 US, with this offer. Offer cannot be combined with any other special and expires 3/31/03. Visit www.trusecure.com/ticsa for full details.
“With Windows 10, Microsoft will be adding a new layer of protection against dynamic script-based malware and non-traditional avenues of cyberattack: the Antimalware Scan Interface (AMSI). AMSI is a “generic interface standard that allows applications and services to integrate with any antimalware product present on a machine.” The interface is there for application developers and antivirus vendors to use. The former can have their app call it if they want some extra scanning and analysis of potentially malicious content. “While the malicious script might go through several passes of deobfuscation, it ultimately needs to supply the scripting engine with plain, unobfuscated code. When it gets to this point, the application can now call the new Windows AMSI APIs to request a scan of this unprotected content,” Lee Holmes, MMPC Principal Software Engineer, explained. “While we’ve been talking about this in the context of scripting engines, it doesn’t need to stop there. Imagine communication apps that scan instant messages for viruses before ever showing them to you or games that validate plugins before installing them.” Third-party developers of antimalware products should seriously consider implementing support for AMSI, as their engine can gain insight into the data that applications (including Windows built-in scripting hosts) consider potentially malicious. Users do nothing, except from benefiting directly from the developers’ decision to used AMSI.”
Security requirements applicable to the Internet of Things (IoT) should aim to ensure integrity, authenticity and authorization, confidentiality/privacy, nonrepudiation, and last but not least, availability. Classic data analysis algorithms are no longer valid for assuring security at all levels and a new approach to data sciences is required, which would consider the complex heterogeneous nature of the IoT, taking also into consideration, its potential to deploy cross layer for security assessment mechanisms. Furthermore, data collected from sensors should be processed and analyzed nearly in real time. The classical algorithms have two main drawbacks: 1) they deal with unidimensional data and 2) they fail to assume limited information available in the stream data processing. In this article, new solutions are discussed and presented that detect anomalies in data streams nearly in real time. Specifically, we propose: 1) an event detection method used in unidimensional data streams and relying on the event strength function, which is an extension of the typical 'True or False' decision-making scheme; 2) multiple-criteria event detection approaches based on the Dynamic Pareto Set, introducing a time-depending decision set; and 3) an anomaly detection method based on multicriteria temporal graphs, combining the dynamics of decision making and multicriteria. All the proposed algorithms are presented by means of their formal description and are illustrated with examples. - Anomaly detection - data mining - Internet of Things (IoT) security - multicriteria decision making
Blue Team \| Investigating Malware and Spam with Wireshark We covered a analyzing an incident with Wireshark. We used Wireshark filters to investigate and reveal malware and its activity. Subscribe to my YouTube channel membership to receive cybersecurity notes. It includes notes about wireshark as well. Eric Fischer from the Purchasing Department at Bartell Ltd has received an email from a known contact with a Word document attachment. Upon opening the document, he accidentally clicked on “Enable Content.” The SOC Department immediately received an alert from the endpoint agent that Eric’s workstation was making suspicious connections outbound. The pcap was retrieved from the network sensor and handed to you for analysis. Task: Investigate the packet capture and uncover the malicious activities. *Credit goes to Brad Duncan for capturing the traffic and sharing the pcap packet capture with InfoSec community. What was the date and time for the first HTTP connection to the malicious IP? (answer format: yyyy-mm-dd hh:mm:ss) What is the name of the zip file that was downloaded? Without downloading the file, what is the name of the file in the zip file? What is the name of the webserver of the malicious IP from which the zip file was downloaded? What is the version of the webserver from the previous question? Malicious files were downloaded to the victim host from multiple domains. What were the three domains involved with this activity? Which certificate authority issued the SSL certificate to the first domain from the previous question? What is the domain name of the post-infection traffic? What are the first eleven characters that the victim host sends out to the malicious domain involved in the post-infection traffic? What was the Server header for the malicious domain from the previous question? The malware used an API to check for the IP address of the victim’s machine. What was the date and time when the DNS query for the IP check domain occurred? (answer format: yyyy-mm-dd hh:mm:ss UTC) What was the domain in the DNS query from the previous question? How many packets were observed for the SMTP traffic?
The operators behind the Shade Ransomware (Troldesh) shut down their operations and released over 750,000 decryption keys. Good news for the victims of the infamous Shade Ransomware, the operators behind the threat have shut down their operations and released over 750,000 decryption keys. The cybercrime gang also apologized for the damages they have caused their victims Shade was considered one of the most dangerous threats in the cyber crime scenario, it has been active at least since 2014 when a massive infection was observed in Russian. Unlike other ransomware strains that don’t encrypt victims in Russia and other CIS countries, Shade also targets computers in Russia and Ukraine. The Shade infections increased during October 2018, keeping a constant trend until the second half of December 2018, taking a break around Christmas, and then resuming in mid-January 2019 doubled in size. Moth of the victims belongs to high-tech, wholesale and education sectors. Shade was distributed through malspam campaigns and exploit kits, once a Windows system gets infected with this ransomware, the malicious code sets the desktop background to announce the infection. The ransomware also drops on the Desktop 10 text files, named README1.txt through README10.txt, The README.txt files include instructions to contact the crooks via an email address in order to receive information on how to make the payments. Michael Gillespie, the creator of the ransomware identification site ID Ransomware, told BleepingComputer that submissions related to the Shade Ransomware decreased since the end of 2019 when Shade Ransomware operators created a GitHub repository and announced that they stopped distributing the ransomware at the end of 2019. The Shade ransomware operators apologized for their activities and provided instructions on how to recover files using the decryption keys they have released. “We are the team which created a trojan-encryptor mostly known as Shade, Troldesh or Encoder.858. In fact, we stopped its distribution in the end of 2019. Now we made a decision to put the last point in this story and to publish all the decryption keys we have (over 750 thousands at all). We are also publishing our decryption soft; we also hope that, having the keys, antivirus companies will issue their own more user-friendly decryption tools.” reads the message published by the gang on GitHub. “All other data related to our activity (including the source codes of the trojan) was irrevocably destroyed. We apologize to all the victims of the trojan and hope that the keys we published will help them to recover their data.” The repository included a decryptor, five master decryption keys, over 750K individual victim’s decryption keys, and of course the instructions on how to use them. Researchers from Kaspersky Lab confirmed that the decryption keys are valid and are including them in the company RakhniDecryptor ransomware decryption tool. Please give me your vote for European Cybersecurity Blogger Awards – VOTE FOR YOUR WINNERS
Detecting Internet Worms Using Data Mining Techniques - Muazzam Siddiqui, Morgan C. Wang, Joohan Lee - Journal of systemics, cybernetics and informatics ico_openaccess - 공학 > 컴퓨터공학, 공학 > 전자/정보통신공학 - Data Mining, Disassembly, Instruction Sequences, Worm Detection, Static Analysis, Binary Classification, Electronic computers. Computer science, QA75.5-76.95, Instruments and machines, QA71-90, Mathematics, QA1-939, Science, DOAJ:Computer Science, DOAJ:Technology and Engineering Internet worms pose a serious threat to computer security. Traditional approaches using signatures to detect worms pose little danger to the zero day attacks. The focus of malware research is shifting from using signature patterns to identifying the malicious behavior displayed by the malwares. This paper presents a novel idea of extracting variable length instruction sequences that can identify worms from clean programs using data mining techniques. The analysis is facilitated by the program control flow information contained in the instruction sequences. Based upon general statistics gathered from these instruction sequences we formulated the problem as a binary classification problem and built tree based classifiers including decision tree, bagging and random forest. Our approach showed 95.6% detection rate on novel worms whose data was not used in the model building process. 조회 가능한 데이터가 없습니다. 해당 참고문헌을 고객센터를 통해 등록해주시면 빠른 시간안에 노출하겠습니다.
- WordPress is prone to multiple vulnerabilities, including cross-site scripting, SQL injection, forgotten password and information disclosure vulnerabilities because it fails to properly sanitize user-supplied input. An attacker may leverage these issues to execute arbitrary script code in the browser of an unsuspecting user in the context of the affected site, to steal cookie-based authentication credentials, to manipulate mail messages, to obtain sensitive information or to compromise the application, access or modify data or to exploit vulnerabilities in the underlying database. WordPress versions prior to 184.108.40.206 are affected. - Update to WordPress version 220.127.116.11 or latest - WordPress Plugin Trust Form Cross-Site Scripting (2.0) - Joomla! Core 1.0.x Unspecified Vulnerability (1.0.0 - 1.0.3) - WordPress Plugin NEX-Forms-Ultimate Form builder Multiple SQL Injection Vulnerabilities (4.0) - WordPress Plugin Gravity Forms SQL Injection (18.104.22.168) - WordPress Plugin 404 Plugin for WordPress SQL Injection (1.0)
Hotmail Forensics – An Overview Overall Investigation of Hotmail mailbox components for analyzing hidden evidences is referred to as Hotmail Forensics Analysis. One of the most important elements that help investigators in cyber findings is “Email Header”. Apart from this analyzing Server logs, the application via which the email has been sent etc. are some of the tactics that plays a vital role in forensic investigation of Hotmail email view. Analyze Hotmail Emails from Forensics Viewpoint In this section, parameters associated with Hotmail email analysis and investigation have been discussed. Since hotmail email forensics is a vast arena, some of the majorly used techniques have been elaborated to understand its widespread properties. Analyzing Hotmail Email Header From Email Header, the investigation authorities may be able to identify the IP address that can further be used to determine sender’s details. In some cases, masking, redirecting and spoofing techniques are used by the sender to prevent its accurate information. Email headers are categorized into two main sections, they are: Envelop Header: This is comparatively more important as it is difficult to spoof the information that comes under this section. This information contains Sender’s Mail Server information, Message-ID as well as X-Message Info fields. Message Header: The information comprised under this section is generally user-defined and can be spoofed easily. This section contains fields such as To, From, Subject, Return-Path, Content-Type, etc. In Hotmail, now known as Outlook.com, the email header forensics can be accessed by navigating through the following procedure: - On webpage, login to your Hotmail account and open message list. - Right click the message and then select “View Message Source” option. This will navigate to the source code page of the email message which will look like the following image. Note: Due to the lengthy text, the code from header section has been copied in the Notepad for clarity purpose. The information mentioned under this section is not considered standard header content as it may not be important for delivery of email messages. How these details helps in investigation, depends upon various techniques adopted by the authorities. This section might reveal the information about Provider ISP. This information is normally configured by user itself in the email client and may or may not be fully reliable. But from forensics point of view, it is important to investigate all possible information as it might help identifying hidden evidences in a way or other. In the source code, it can clearly be seen that the ‘Received’ section has been repeated thrice. Investigators study these parameters from bottom to top to view hotmail emails forensically. It shows the transaction path through which the email message has been traversed. - The last ‘Received’ value is considered first in the queue of hotmail forensics analysis. This contains information about the sender computer with IP address as well as the details of the sender’s Mail Server. The provided IP Address is further analyzed to know the location of the sender of the email. Note: The date and time information mentioned in this section is sent off by the Email Sever and might not consider corresponding to the time when the email is sent by the sender. - The second ‘Received’ or the subsequent same field demonstrate information about the path through which the specified email message has been travelled. - The first yet the least from investigator’s point of view will show the IP address for the target Server or the recipient. This section of the header text shows that the email is MIME (Multi-Purpose Internet Mail Extensions) formatted. It ensures that the sent email message is compliant to RFC 1341 standard formatting. MIME displays a registration policy that uses IANA (Internet Assigned Numbers Authority) as a registry for all associated standards and values. - DKIM-Signature: DomainKeys Identified Mail It is a technique to validate the authenticity of any email message for detecting email spoofing. If an email is sent off by an organization with DKIM signature assigned on the mail, it denotes that the message is not a SPAM and the signing authority is directly responsible to it. Likewise ‘X-Original Arrival Time’, this field also comes under ‘X-headers’ that falls under the category of nonstandard headers. However, this might explores information about (ISP) Internet Service Provider, so investigators do not take this section lightly and thorough investigation is done considering all associated factors. - Authentication-Results: (SPF) If an email is sent using a domain, it is the responsibility of the SPF (Server Policy Framework) to check if the specified Mail Server is authorized to send email messages to that particular domain. The possible result set could be ‘PASS’, ‘FAIL’ or ‘NONE’. In Hotmail message header section, if the email message is delivered successfully to the recipient, this can be read as: However, in case, if the domain is not registered under that particular Mail Server, this value may display as ‘NONE’. And if the sender’s Mail Server fails to deliver message to the recipient, the value might return as ‘FAIL’. This is referred to as the detailed analysis of Server logs as well as the delivered email messages. The emails purged from senders or recipients specially those that are hard to recover at the same; can be requested from Hotmail Server or Internet Service Provider (ISP) as the replica is stored by them for each delivered email message. Thereafter, the logs can be analyzed for tracking the original address of the sender computer. Here, the limitation is that the replica for email logs and the messages are maintained by the Server for stipulated period of time and this might proved to be an obstacle for the forensic investigators. The Hotmail may contain information about the sender of the email message and this can be revealed from the sent message(s) folder, the attached documents or the enclosed attachments. This information might have been accumulated in the custom header components or as MIME content in TNEF (Transport Neutral Encapsulation Format). Forensic Analysis of Hotmail mailboxes may result in gathering vital information and details about the sender. This investigation might reveal the information about Windows’ logon username, IP Address, etc. for the client machine that is been used for sending the email message. Thorough analysis of all above mentioned components following well defined criteria can help investigators in Hotmail Forensics of emails. However, spoofed email headers; messages sent from remote locations such as airports, libraries, internet cafes; delayed delivery of email messages are some of the issues that investigators must be aware of as they might mislead the officials while investigating evidences.
Cloud providers such as Microsoft Azure, Amazon and Google are perennial targets for attackers seeking to compromise and weaponize virtual machines and other resources.The digital age opens so many opportunities but also very serious threats. Once compromised the attacker can use these platforms to use these virtual machines to launch attacks, including brute force attacks against other virtual machines, to deliver spam campaigns that can be used for email phishing attacks, for reconnaissance such as port scanning to identify new attack targets, and for other malicious activities. The geographical map within the link below shows incoming attacks on Azure—specifically the IP addresses where the attacks originated – detected by Azure Security Center. Interesting is the comparison of different countries. In a cloud weaponization threat scenario, an attacker establishes a foothold within a cloud infrastructure by compromising and taking control of one or more virtual machines. Once compromised, those resources often communicate with command-and-control (C&C) servers to receive instructions. In 2019 Outgoing attacks % (outgoing communications from these countries going out to malicious IP addresses was as follows: Interestingly the number of Drive By Downloads (DBD) is the act of unintentional download of malicious code onto an unsuspecting user’s computer when they visit a web site. The malicious code could be used to exploit vulnerabilities in web browsers, browser add-ons, applications, and the operating system. Users can be infected with malware simply by visiting a website, even without attempting to download anything.Worldwide these encounters have decreased 22% between January and December 2018. If you would like to discuss your business security please contact us for a general chat. Once we understand any concerns we can help advise. Click here for more information
Data-driven companies need a data loss prevention (DLP) strategy to protect their valuable information. Enterprises must guard against data being compromised, lost, or misused deliberately or accidentally. The same level of damage can be caused by a data breach initiated by a cyberattack or one triggered by an employee’s accidental disclosure of intellectual property via unencrypted email. Implementing a successful DLP strategy requires a methodical approach that addresses the needs of the business and the type of data it gathers, stores, and processes. The following steps illustrate the best practices that should be part of a company’s DLP strategy. Create a data handling policy A company’s data handling policy must align with business requirements and reflect its information resources. If a company has a typical mix of data assets, it may be able to use a data handling policy template that can be tailored to the organization’s specific needs. This policy defines classes of data that require different types of handling to protect the organization. Typically, at least three kinds of data are classified based on the information’s importance and the damage its misuse or loss can cause: High-risk data includes sensitive data and intellectual property that can cause extensive damage if lost or compromised. If the company operates in a regulated industry, the data handling policy has to address issues such as compliance with security and privacy standards and regulations such as HIPAA and GDPR. Information subject to regulatory standards will almost always fall into the high-risk category. Medium-risk data can cause less extensive damage to an organization. It may include operational guides that might interest competitors but will not negatively affect the business. Low-risk data does not need additional protection and can be freely distributed to the public. Informative guides and user manuals that help customers use a company’s products are examples of low-risk data. The policy will define how differently classified data elements are handled within the company. For example, it may specify that all high and medium-risk data be encrypted before transmission over public networks. It may restrict the use of sensitive information to a small group of authorized users. The data handling policy is the foundation upon which a DLP strategy is built, so it’s worth taking the time to get it right. Classify all data resources Once a data handling policy has been created, a company’s information resources must be classified. This includes discovering data assets across the entire computing environment so they can be classified. Legacy DLP solutions required data discovery and classification to be performed manually. However, modern DLP tools like Next DLP’s Reveal automate this process by dynamically discovering and classifying data for enhanced accuracy, eliminating the need for additional classification tools. Identify data vulnerabilities Companies must identify situations or activities that present data vulnerabilities. This is often an ongoing activity that evolves as a DLP strategy matures or business requirements change. The discovered vulnerabilities offer focus points for enforcing the data handling policy. Examples of data vulnerabilities include: Activities that demand the sharing of sensitive data via email Transmitting high-risk data from remote endpoints Storing sensitive data in public cloud storage The next step of the DLP strategy addresses these vulnerabilities. Enforce the data handling policy The main purpose of a data loss prevention strategy is to enforce the company’s data handling policies. A reliable DLP solution should be capable of enforcing the policy through the infrastructure. It should be equally effective in handling newly created data, data residing in legacy systems, and data recently ingested into the environment. Automated enforcement will perform activities to protect data before it is lost or misused. Examples of the enforcement of a data handling policy include: - Automatically encrypting high-risk data at all times - Preventing unauthorized users from viewing or printing sensitive data - Classifying data effectively as it is incorporated into the environment Monitor data movement Continuous monitoring of data movement is critical for a successful DLP strategy. Every time data is exchanged, transferred, or accessed, it should be subject to the data handling policy. This includes all internal and external uses of data resources. Provide ongoing education Employees should be provided with ongoing education regarding the enterprise data handling policy and their role in protecting information resources. Ideally, a DLP solution offers real-time guidance when a policy violation occurs so the individual understands how they need to modify their behavior in the future. Deploying a modern DLP solution Next DLP offers customers a human-centric DLP solution that discovers risks, remediates them by enforcing a data handling policy, and educates its users. Our Reveal product is a cloud-native solution that is easy to install and use. It provides immediate results with configurable data handling policies and machine learning, provides smart remediation and enforces the policy even when computers are disconnected from the network. Reveal employs lightweight agents for Windows, Linux, and macOS computers that won’t impact performance or productivity. Real-time, incident-based training is also furnished to employees to help improve their understanding of the data handling policy. Contact the Next team or book a demo to see how easy it is to get started with a successful data loss prevention strategy.
Recently biometric sought more attention in the field of research and development. Mouse dynamics otherwise known as click dynamics is a biometric system which plays a predominant role in the field of network security. In this paper, enhanced click dynamics is introduced to ensure user authentication. The unknown cyber-attack detection process is developed using biometric system. In this paper, "Anytime algorithm" is integrated along with existing method to increase the accuracy in detecting the unauthorized user i.e., cyber-attacks. To evaluate the accuracy in detecting the unknown cyber-attacks by the proposed method the experiments were conducted with few performance metrics namely false acceptance rate, false rejection rate, authentication time and attack detection rate.
BehavioSec - Continuous Authentication The BehavioSec platform provides a continuous and transparent sensory capability that helps verify that people online are who they say they are. It analyzes users’ keystrokes, cursor movements, screen pressure, and device handling to continuously authenticate users based on their innate behaviors in real time. The system is invisible to end users. End users interact with the app in their normal ways, and their normal usage patterns are gathered. In the background on the client app, the BehavioSec SDK collects data on how a user interacts with the app. A proxy mechanism on the backend passes the data to the BehavioSense Server, a machine learning framework, that then creates a user-specific Profile. Once a Profile is established, BehavioSense compares real-time data with previously saved data in the Profile. It analyzes the data to get an assessment of the similarity between the old and the new behavior. In this way BehavioSec can identify whether the user is the expected user. This analysis is reflected in the Behavioral Score, along with other results. The platform can also identify behavioral patterns that may indicate a bot or RAT or other possibly malicious activity. In addition, it can capture non-behavioral descriptive data, such as IP addresses and mobile platform details, for additional types of analysis. You can use the BehavioSec platform with any type of app where an end user inputs text, interacts with touch surfaces, or uses a mouse. An SDK captures the end user’s keystroke or pressure, swipes, etc. The system provides continuous authentication when multiple input fields and buttons throughout an app are instrumented. Using BehavioSense for Continuous Authentication Conventional approaches to security bring a need to choose between robust protections and streamlined user experience, and now there is a shift from these legacy systems to layered, adaptive approaches. BehavioSense is a layer in that security process that can authenticate users based on their own behaviors, using sophisticated data collection, without interrupting the user experience. BehavioSense provides passive verification and makes it more difficult for bad actors to mimic or compromise the security of the interaction. For example, when an app's user types in various fields or clicks on various buttons, BehavioSense creates a Behavioral Score that indicates how close the user's behavioral patterns are to previously stored patterns. If BehavioSense indicates a low similarity score, an integrated application or third party security system can respond to that potential risk. If the low score is triggered on a login, for example, the system can respond with a step-up in authentication. The step-up can be a one-time password, security question, static biometric authenticator, or other threat-appropriate security measure defined by the customer's security infrastructure. Even after login, BehavioSense can analyze activity in other fields and buttons during a user session. Continuous authentication provides additional security when there is a risk of a device being compromised mid-session. Using BehavioSense for Fraud Detection and Analysis BehavioSense is part of a multi-layered fraud analysis system. It adds dynamic behavioral biometrics to provide continuous user authentication. BehavioSense gathers and analyzes behavioral data and provides a Behavioral Score, among other metrics. The Behavioral Score indicates whether the current user is the same as the expected user. For more information about the metrics, see the BehavioSec documentation Understanding Behavioral Scores and Metrics. In a conventional enterprise security system, the volume of alerts makes it difficult to distinguish false alarms from real risks. BehavioSense helps security teams to prioritize threats and improve efficiency in identifying breaches such as account takeovers, bots, or troll and spam accounts. In the case of a fraudulent session, the system analyzes and identifies the characteristics of the fraud or breach type. A security team can see both an audit trail and fraud breakdown in the BehavioSense Dashboard Risk Flags, and/or use the BehavioSense REST API to pull the data into another security system component. Flags can include: - A change in IP address - A change in device during a session - A Remote Access Trojan (RAT) in the browser - Bot activity - Replay attacks - And more... see the BehavioSec documentation for Risk Flags. Please familiarize yourself with the BehavioSec SDK and ask customer Support for a deep dive into the flag and configuration options. Download the latest release from https://github.com/ForgeRock/BehavioSec/releases/latest and copy file to the ../web-container/webapps/openam/WEB-INF/lib directory where AM is deployed. Restart the web container to pick up the new node. The node will then appear in the authentication trees components palette. The code in this repository has binary dependencies that live in the ForgeRock maven repository. Maven can be configured to authenticate to this repository by following the following ForgeRock Knowledge Base Article. Make sure to contact Sales Representative for licensing. The following sections provides information about configuring the BehavioSense tree. The BehavioSec API backend returns a JSON response that has been integrated to handle the outcome in ForgeRock Nodes. To start behavior metrics authentication follow these steps: - Obtain the URL to the BehavioSense API endpoint, dashboard URL, and get access to developer portal documentation. - Create a user with an email address and real password (see Note 1). - Combine the components as show in the Authentication Tree. - Navigate to the login page with the tree URL + - Login in as the user. - Verify with the BehavioSense Dashboard recorded session. Note 1: BehavioSec machine learning is developed on real scenarios, therefore it is highly recommended to use more than 8 characters each for the user name and password. On profile training BehavioSec provides all the necessary components to use the BehavioSec platform platform out the box. A sample of the Authentication Tree is shown below. Details for component configuration are in the following sections. Naturally, Failure outcomes should result in authentication step up, retry, or even account lock out. This is a data collector node that you need to place under the page node. In the configuration you have an option to add a different collector script if needed. This node receives the collected data and communicates with the server. Hash username - if set to true, the username will be hashed using Anonymize IP - if set to true, set last octet of IP address to 000. Fail if no connection - option allows node evaluation to true even if the connection to BehavioSense was not established. BehavioSec Score Evaluator This Score evaluation module allows you to specify the Behavioral Score, Confidence, and Risk levels. Anything below the specified values will fail. It also allows you to control the outcome for users whose Profiles are still in the Training phase. Behavioral Score or Score is a numerical value ranging from 0 to 100, that indicates to what degree the timing data in the session matches the timing data in the trained Profile. A high Behavioral Score means there is little difference between the behavior in the session and the user’s Profile. Read more about the Behavioral Score. Confidence is a value that represents the quantity of data that has been stored in a Profile and is available to check against the user. The higher the Confidence value, the more data is available to check against the behavior presented in a given session. Read more about the Confidence value. Risk is a numerical measure of potentially fraudulent activity during the course of a user session. It can be a number greater than or equal to zero. A Risk value in the range of 0-100 is considered minimal risk, while over 100 is high risk and should be investigated for fraud. Read more about the Risk value. Allow In Training indicates that the user Profile is still in the Training phase. If enabled, the Score and Risk will be ignored and the node will evaluate to true. BehavioSec Boolean Evaluator The Boolean evaluator controls the outcome for flags returned by the BehavioSense module. It will fail on any condition evaluating to false. Boolean Flag configuration - Bot Detection indicates that robotic behavior was detected, such as a typing rhythm that is too uniform or jittery mouse movements. This information is received from the isBot flag in the JSON. Allow Auto-Login Bot enabled evaluates to a true outcome even if a bot is detected. Default is false. - Replay Attack indicates that the exact same behavioral data has been received in the past. This information is received from the isReplay flag. Allow Replay enabled evaluates to a true outcome even if replay is detected. Default is false. - Allow In Training indicates that the user Profile is still in the Training phase. If enabled, the Score and Risk will be ignored and the node will evaluate to true. - Remote Access indicates that one or more remote access protocols were detected in the session. If remote access has been flagged, you can see a breakdown of software using the detected protocols by looking at the ratProtocol parameter. Allow Remote Access enabled evaluates to a true outcome even if a remote access protocol is detected. Default is true. - Tab Anomaly indicates the the user has inconsistent tabbing behavior. This information is received from the tabAnomaly flag in the JSON. Allow Tab Anomaly enabled evaluates to a true outcome even if a tab anomaly is detected. Default is true. - Numpad Anomaly indicates that the user has inconsistent numeric keypad behavior. This information is received from the numpadAnomaly flag in the JSON. Allow Numpad Anomaly enabled evaluates to a true outcome even if a numpad anomaly is detected. Default is true. - Device Changed indicates that the device/user agent string has changed during the active session. When a new device type is detected (e.g., Desktop, Android, or iOS device), this flag is set to true. This information is received from the deviceChanged flag in the JSON. Allow Device Change enabled evaluates to a true outcome even if a device change is detected. Default is true. For full list of available flags please see the BehavioSec documentation for Risk Flags. The sample code described herein is provided on an "as is" basis, without warranty of any kind, to the fullest extent permitted by law. BehavioSec does not warrant or guarantee the individual success developers may have in implementing the sample code on their development platforms or in production configurations. BehavioSec does not warrant, guarantee or make any representations regarding the use, results of use, accuracy, timeliness or completeness of any data or information relating to the sample code. BehavioSec disclaims all warranties, expressed or implied, and in particular, disclaims all warranties of merchantability, and warranties related to the code, or any service or software related thereto. BehavioSec shall not be liable for any direct, indirect or consequential damages or costs of any type arising out of any action taken by you or others related to the sample code.
Network attack graphs are directed graph-representations of possible attack paths and vulnerabilities in a computer network. Each attack path is a sequence of steps taken by an attacker to achieve one or more goals in the target system. While there are some variations in the representations of the graph proposed by different researchers, typically the edges represent possible actions (or exploits) available to an attacker, and vertices represent the possible states for the system and applications. Attack graphs are often manually created or, less often, automatically generated from a set of attack models and detailed information about the network topology and its applications. There have been several proposals for the automatic identification and representation of attack models, but they all rely on some prerequisite knowledge of the pre- and post-conditions for the different attack steps. A pre-condition may include requirements such as "attacker must have root privileges", while a post-condition defines the state of the system after an action is taken. In this paper we propose algorithms for the automatic identification of likely pre- and post-conditions that can be used for the generation of attack graphs. Our approach extracts such candidate conditions from observational data. By monitoring low-level events on multiple network nodes, in correlation with detected anomalies or attacks, our approach can automatically and unobtrusively identify the attributes of interest for the attack model required for attack graph generation. The paper provides a brief review of the requirements for automatic attack graph generation, and describes our proposed approach in detail. We also present preliminary simulation results for the automatic discovery of attack messages and their pre- and post-conditions, in a simplified fully connected network environment. \|Title of host publication\|\|Proceedings of the International Conference on Information-Warfare and Security\| \|Publication status\|\|Published - 1 Jan 2010\| \|Event\|\|International Conference on Information-Warfare and Security (ICIW) - \| Duration: 1 Jan 2010 → … \|Conference\|\|International Conference on Information-Warfare and Security (ICIW)\| \|Period\|\|1/01/10 → …\|
One-day Application Security Assessment Meristem will spend a single day testing a website or application for the most common application security flaws, simulating a lightly motivated attacker looking for an easy target. This level of test should not satisfy external requirements for a penetration test, but will identify critical control gaps and should give application owners some peace of mind. Meristem will also identify features of the application that commonly contain security flaws and provide points to consider when securing these features. Standard Application Security Assessment An examination of the security controls in place for a website or application based on Level 2 of OWASP’s Application Security Verification Standard (ASVS) with additional checks based on current application security threats and Meristem’s industry expertise. This level of test satisfies most requirements for an application penetration test, including PCI DSS requirements. Application Security Assessment with Source Code Review Meristem will perform a Standard Application Security Assessment based on Level 2 of OWASP’s Application Security Verification Standard, with the same additional checks, and will also review the source code for the application for common errors or implementation mistakes. With this additional access, Meristem is able to be more confident in the results and in many cases definitely state that an application does not contain specific vulnerability types. Source code review enables Meristem to efficiently identify corner cases in application logic, validation routines, and data handling practices that are difficult to find during the limited timeframe of a standard assessment, but could be discovered by an attacker through chance or persistent effort. Enhanced Application Security Assessment An examination of the security controls in place for an application based on Level 3 of OWASP’s Application Security Verification Standard with additional checks based on current application security threats and Meristem’s industry expertise. This additional level of examination is appropriate for highly sensitive applications that must withstand significant scrutiny by both users and attackers. The assessment requires access to the application source code and interviews with application architects and developers. Security Design Review A security design review is similar to a tabletop penetration test. Meristem will conduct a short series of meetings with those who know your application best (typically architects, lead developers, product owners) to identify the key components, data flows, assets, and threat actors. Based on that view, the group will identify the controls needed on each data flow to adequately protect the assets, and identify potential control gaps. This review can be done on an existing application, or ideally, when an application is just completing the design phase.
Code Analysis for Managed Code Overview Code analysis for managed code tool analyzes managed assemblies and reports information about the assemblies, such as violations of the programming and design rules set forth in the Microsoft .NET Framework Design Guidelines. The analysis tool represents the checks it performs during an analysis as warnings. Warning messages identify any relevant programming and design issues and, when possible, supply information about how to fix the problem. IDE (integrated development environment) Integration To make it convenient for developers to use the analysis tool, developers can select Enable Code Analysis on the project's Property Pages. Additional options for including or excluding rules and treating rules as warnings or errors can also be accessed from property pages. When the tool is enabled, during the build process, it reports warnings in the Error List. In Source Suppression It is often useful to indicate that a warning is non-applicable; this informs the developer, and others who might review the code later, that a warning was investigated and then either suppressed or ignored. In Source Suppression of warnings is implemented through custom attributes. To suppress a warning, add the attribute SuppressMessage to the source code as shown in the following example: Public class MyClass For more information, see. Run code analysis tool as part of check-in policy As an organization, you might want to require that all check-ins satisfy certain policies. In particular, you want to make sure that you follow these policies: There were no build errors in code being checked in. Code analysis was run as part of the most recent build. You can accomplish this by specifying check-in policies. For more information, see. Team System Team Build Integration You can use the integrated features of the build system to run the analysis tool as part of the build process. For more information, see.
What is Trojan-Ransom.Win32.Gen.kfx infection? In this post you will certainly find regarding the meaning of Trojan-Ransom.Win32.Gen.kfx and its unfavorable effect on your computer system. Such ransomware are a form of malware that is elaborated by on-line fraudulences to require paying the ransom money by a victim. It is better to prevent, than repair and repent! Most of the situations, Trojan-Ransom.Win32.Gen.kfx infection will certainly instruct its targets to launch funds move for the objective of reducing the effects of the changes that the Trojan infection has actually introduced to the target’s tool. These modifications can be as adheres to: - Reads data out of its own binary image; - Uses Windows utilities for basic functionality; - Deletes its original binary from disk; - Exhibits possible ransomware file modification behavior; - Network activity detected but not expressed in API logs; - Clears Windows events or logs; - Clears web history; - Uses suspicious command line tools or Windows utilities; - Ciphering the papers found on the sufferer’s hard disk — so the target can no more make use of the data; - Preventing regular access to the target’s workstation; One of the most common networks through which Trojan-Ransom.Win32.Gen.kfx Ransomware Trojans are infused are: - By methods of phishing emails; - As a repercussion of individual ending up on a resource that holds a malicious software application; As soon as the Trojan is effectively infused, it will certainly either cipher the data on the victim’s computer or prevent the gadget from working in a correct way – while additionally putting a ransom money note that mentions the demand for the targets to effect the repayment for the function of decrypting the documents or bring back the file system back to the preliminary condition. In a lot of instances, the ransom money note will certainly come up when the customer reboots the PC after the system has actually currently been damaged. Trojan-Ransom.Win32.Gen.kfx distribution channels. In various edges of the world, Trojan-Ransom.Win32.Gen.kfx grows by jumps and also bounds. However, the ransom notes as well as tricks of extorting the ransom money amount might vary depending on specific regional (regional) settings. The ransom money notes and also methods of extorting the ransom quantity might vary depending on certain neighborhood (regional) settings. Faulty notifies regarding unlicensed software. In particular locations, the Trojans usually wrongfully report having actually found some unlicensed applications made it possible for on the sufferer’s device. The sharp then demands the individual to pay the ransom. Faulty statements concerning prohibited content. In countries where software program piracy is less popular, this technique is not as efficient for the cyber frauds. Additionally, the Trojan-Ransom.Win32.Gen.kfx popup alert might wrongly claim to be stemming from a police institution and will report having located kid porn or other prohibited information on the device. Trojan-Ransom.Win32.Gen.kfx popup alert might wrongly assert to be acquiring from a legislation enforcement organization and will certainly report having situated child porn or other unlawful information on the tool. The alert will similarly have a requirement for the user to pay the ransom money. File Info:crc32: 23599C25md5: 4b4f0e7b285f8ddce6d3a7c71bd53ed4name: 4B4F0E7B285F8DDCE6D3A7C71BD53ED4.mlwsha1: 4e8f0c9c4ee538bcd198688481591b6f892c7153sha256: cf72b4edb1883ff61b18b61179d135f1a0cb00b7f5ad023dc685d72f5d3e1a26sha512: 97adc6e3b8922dc4ad5d62cda1adf8e72bd0c7149e534a347fcdd1bc16803cd34a17e8aff209182455a5392e2a084989be5b489d0f82362df29f8a0e53fb4ccdssdeep: 6144:QsCwu+mWhJifvtNP/7YXSLB80PqO/PhR3pd:NxmIJQvPkitEqZR3pdtype: PE32 executable (GUI) Intel 80386, for MS Windows Version Info:0: [No Data] Trojan-Ransom.Win32.Gen.kfx also known as: \|K7GW\|\|Trojan ( 0051527b1 )\| \|K7AntiVirus\|\|Trojan ( 0051527b1 )\| How to remove Trojan-Ransom.Win32.Gen.kfx 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 Trojan-Ransom.Win32.Gen.kfx 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 Trojan-Ransom.Win32.Gen.kfx you can always ask me in the comments for getting help. User Review( votes)
Deepfakes are an emerging kind of cyberattack, and as such, there are no effective countermeasures in place as yet. Preventive measures such as the three listed below are being investigated. If you are blackmailed by التزييف العميق, you can contact us. Technology for Attack Detection Can Help Prevent It An effective defence against deepfake assaults for businesses would be to establish a reliable means to identify them, particularly using automated technologies. One of these possibilities is the use of AI-powered detecting software. To be more precise, the deep learning algorithms that are used to construct deepfakes may also be used to detect evidence that an image or video has been manipulated in some way. Forensic analysis is used in this step-by-step approach to determine which copy is real and which is modified, as seen below. Deepfakes may be detected in a variety of ways, and this is one of them. Watermarking material to identify tampering is another tech-based technique. Amber Authenticate is a cryptography tool that generates hashes at predetermined intervals throughout a video. The hashes will change if the movie is altered, alerting the user that the material has been altered. We can protect you from الديب فيك very easily. Combative security is provided through safety protocols. Deepfakes may be combated in part by implementing new security policies in your company. While a deepfake has a good chance of fooling a single person, that chance drops dramatically when there are more persons involved. A corporation may detect an attack before it does any harm by adding numerous inspections to instances where deepfakes may be involved. Deepfakes are phone and video call spoofing tools used by hackers pretending to be from a firm. As a result, firms might implement security policies that outline a checking method workers should do when they get such calls in order to identify deepfakes. Employee training results in increased awareness. The uniqueness of the threat is one of the reasons why deepfakes are a concern to businesses. For the most part, companies have no idea what deepfakes are or how damaging they can be. By training employees, management, and owners, firms may lessen their vulnerability to a deepfake assault. Businesses need a methodical strategy to fostering a security-conscious corporate culture. It’s important to educate personnel about security threats and preventative strategies so they can spot deepfakes faster. Deepfakes and Cybersecurity: A New Era in A look into the future of cybercrime is possible thanks to the use of deepfakes. As a result of their actions, they serve as a shining illustration of how technology may cause us to behave in ways that put our affiliations in danger. To make things worse, the danger posed by deepfakes is still being developed right now. This means that businesses must educate their employees about deepfake threats and implement technology solutions as soon as they become available. If they want more assistance, they may work with a cybersecurity firm.
From CENSTACS: Identifies IP hosts potentially scanning a network (commonly used to detect PCs infected with a virus, trojan or worm scanning networks for PCs or servers to infect). Identifies IP hosts engaging in excessive usage of network resources (Internet Web surfing, media streaming, P2P activity, VOIP activity). Identifies IP Hosts visiting Foreign Country Internet resources (commonly used to alert support staff to potential spyware issues). Identifies users logged into Windows based OS PCs and servers associated with the activity (requires integrated authentication environment). Aids in identifying, documenting and tracking general network usage of internal and/or external service and support applications. read more +

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