Metamorphic Viruses with Built-In Buffer Overflow

Metamorphic computer viruses change their structure—and thereby their signature—each time they infect a system. Metamorphic viruses are potentially one of the most dangerous types of computer viruses because they are difficult to detect using signature-based methods. Most anti-virus software today is based on signature detection techniques. In this project, we create and analyze a metamorphic virus toolkit which creates viruses with a built-in buffer overflow. The buffer overflow serves to obfuscate the entry point of the actual virus, thereby making detection more challenging. We show that the resulting viruses successfully evade detection by commercial virus scanners. Several modern operating systems (e.g., Windows Vista and Windows 7) employ address space layout randomization (ASLR), which is designed to prevent most buffer overflow attacks. We show that our proposed buffer overflow technique succeeds, even in the presence of ASLR. Finally, we consider possible defenses against our proposed technique

Image-based malware classification: A space filling curve approach

Anti-virus (AV) software is effective at distinguishing between benign and malicious programs yet lack the ability to effectively classify malware into their respective family classes. AV vendors receive considerably large volumes of malicious programs daily and so classification is crucial to quickly identify variants of existing malware that would otherwise have to be manually examined. This paper proposes a novel method of visualizing and classifying malware using Space-Filling Curves (SFC\u27s) in order to improve the limitations of AV tools. The classification models produced were evaluated on previously unseen samples and showed promising results, with precision, recall and accuracy scores of 82%, 80% and 83% respectively. Furthermore, a comparative assessment with previous research and current AV technologies revealed that the method presented her was robust, outperforming most commercial and open-source AV scanner software programs

Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey

Malwares are big threat to digital world and evolving with high complexity. It can penetrate networks, steal confidential information from computers, bring down servers and can cripple infrastructures etc. To combat the threat/attacks from the malwares, anti- malwares have been developed. The existing anti-malwares are mostly based on the assumption that the malware structure does not changes appreciably. But the recent advancement in second generation malwares can create variants and hence posed a challenge to anti-malwares developers. To combat the threat/attacks from the second generation malwares with low false alarm we present our survey on malwares and its detection techniques.Comment: 5 Page

Classifying malicious windows executables using anomaly based detection

A malicious executable is broadly defined as any program or piece of code designed to cause damage to a system or the information it contains, or to prevent the system from being used in a normal manner. A generic term used to describe any kind of malicious software is Maiware, which includes Viruses, Worms, Trojans, Backdoors, Root-kits, Spyware and Exploits. Anomaly detection is technique which builds a statistical profile of the normal and malicious data and classifies unseen data based on these two profiles. A detection system is presented here which is anomaly based and focuses on the Windows® platform. Several file infection techniques were studied to understand what particular features in the executable binary are more susceptible to being used for the malicious code propagation. A framework is presented for collecting data for both static (non-execution based) as well as dynamic (execution based) analysis of the malicious executables. Two specific features are extracted using static analysis, Windows API (from the Import Address Table of the Portable Executable Header) and the hex byte frequency count (collected using Hexdump utility) which have been explained in detail. Dynamic analysis features which were extracted are briefly mentioned and the major challenges faced using this data is explained. Classification results using Support Vector Machines for anomaly detection is shown for the two static analysis features. Experimental results have provided classification results with up to 94% accuracy for new, previously unseen executables

Evaluation of Malware Target Recognition Deployed in a Cloud-Based Fileserver Environment

Cloud computing, or the migration of computing resources from the end user to remotely managed locations where they can be purchased on-demand, presents several new and unique security challenges. One of these challenges is how to efficiently detect malware amongst files that are possibly spread across multiple locations in the Internet over congested network connections. This research studies how such an environment will impact the performance of malware detection. A simplified cloud environment is created in which network conditions are fully controlled. This environment includes a fileserver, a detection server, the detection mechanism, and clean and malicious file sample sets. The performance of a novel malware detection algorithm called Malware Target Recognition (MaTR) is evaluated and compared with several commercial detection mechanisms at various levels of congestion. The research evaluates performance in terms of file response time and detection accuracy rates. Results show that there is no statistically significant difference in MaTR\u27s true mean response time when scanning clean files with low to moderate levels of congestion compared to the leading commercial response times with a 95% confidence level. MaTR demonstrates a slightly faster response time, by roughly 0.1s to 0.2s, at detecting malware than the leading commercial mechanisms\u27 response time at these congestion levels, but MaTR is also the only device that exhibits false positives with a 0.3% false positive rate. When exposed to high levels of congestion, MaTR\u27s response time is impacted by a factor of 88 to 817 for clean files and 227 to 334 for malicious files, losing its performance competitiveness with other leading detection mechanisms. MaTR\u27s true positive detection rates are extremely competitive at 99.1%

Emerging & Unconventional Malware Detection Using a Hybrid Approach

Advancement in computing technologies made malware development easier for malware authors. Unconventional computing paradigms such as cloud computing, the internet of things, In-memory computing, etc. introduced new ways to develop more complex and effective malware. To demonstrate this, we designed and implemented a fileless malware that could infect any device that supports JavaScript and HTML5. In addition, another proof-of-concept is implemented that signifies the security threat of in-memory malware for in-memory data storage and computing platforms. Furthermore, a detailed analysis of unconventional malware has been performed using current state-of-the-art malware analysis and detection techniques. Our analysis shows that, by utilizing the unique characteristics of emerging technologies, malware attacks could easily deceive the anti-malware tools and evade themselves from detection. This clearly demonstrates the need for an innovative and effective detection mechanism. Because of the limitations of existing techniques, we propose a hybrid approach using specification-based and behavioral analysis techniques together as an effective solution against unconventional and emerging malware instances. Our approach begins with the specification development where we present the way of writing it in a succinct manner to describe the expected behavior of the application. Moreover, the behavior monitoring component of our approach makes the detection mechanism effective enough by matching the actual behavior with pre-defined specifications at run-time and alarms the system if any action violates the expected behavior. We demonstrate the effectiveness of the proposed approach by applying it for the detection of in-memory malware that threatens the HazelCast in-memory data grid platform. In our experiments, we evaluated the performance and effectiveness of the approach by considering the possible use cases where in-memory malware could affect the data present in the storage space of HazelCast IMDG