Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks

Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause systems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form of viruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. Typically, anti-virus software is based on a signature definition system which keeps updating from the internet and thus keeping track of known viruses. While this may be sufficient for home-users, a security risk from a new virus could threaten an entire enterprise network. This paper proposes a new and more sophisticated antivirus engine that can not only scan files, but also build knowledge and detect files as potential viruses. This is done by extracting system API calls made by various normal and harmful executable, and using machine learning algorithms to classify and hence, rank files on a scale of security risk. While such a system is processor heavy, it is very effective when used centrally to protect an enterprise network which maybe more prone to such threats.Comment: 6 page

BlogForever: D3.1 Preservation Strategy Report

This report describes preservation planning approaches and strategies recommended by the BlogForever project as a core component of a weblog repository design. More specifically, we start by discussing why we would want to preserve weblogs in the first place and what it is exactly that we are trying to preserve. We further present a review of past and present work and highlight why current practices in web archiving do not address the needs of weblog preservation adequately. We make three distinctive contributions in this volume: a) we propose transferable practical workflows for applying a combination of established metadata and repository standards in developing a weblog repository, b) we provide an automated approach to identifying significant properties of weblog content that uses the notion of communities and how this affects previous strategies, c) we propose a sustainability plan that draws upon community knowledge through innovative repository design

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

On the Reverse Engineering of the Citadel Botnet

Citadel is an advanced information-stealing malware which targets financial information. This malware poses a real threat against the confidentiality and integrity of personal and business data. A joint operation was recently conducted by the FBI and the Microsoft Digital Crimes Unit in order to take down Citadel command-and-control servers. The operation caused some disruption in the botnet but has not stopped it completely. Due to the complex structure and advanced anti-reverse engineering techniques, the Citadel malware analysis process is both challenging and time-consuming. This allows cyber criminals to carry on with their attacks while the analysis is still in progress. In this paper, we present the results of the Citadel reverse engineering and provide additional insight into the functionality, inner workings, and open source components of the malware. In order to accelerate the reverse engineering process, we propose a clone-based analysis methodology. Citadel is an offspring of a previously analyzed malware called Zeus; thus, using the former as a reference, we can measure and quantify the similarities and differences of the new variant. Two types of code analysis techniques are provided in the methodology, namely assembly to source code matching and binary clone detection. The methodology can help reduce the number of functions requiring manual analysis. The analysis results prove that the approach is promising in Citadel malware analysis. Furthermore, the same approach is applicable to similar malware analysis scenarios.Comment: 10 pages, 17 figures. This is an updated / edited version of a paper appeared in FPS 201

Malware Pattern of Life Analysis

Many malware classifications include viruses, worms, trojans, ransomware, bots, adware, spyware, rootkits, file-less downloaders, malvertising, and many more. Each type may share unique behavioral characteristics with its methods of operations (MO), a pattern of behavior so distinctive that it could be recognized as having the same creator. The research shows the extraction of malware methods of operation using the step-by-step process of Artificial-Based Intelligence (ABI) with built-in Density-based spatial clustering of applications with noise (DBSCAN) machine learning to quantify the actions for their similarities, differences, baseline behaviors, and anomalies. The collected data of the research is from the ransomware sample repositories of Malware Bazaar and Virus Share, totaling 1300 live malicious codes ingested into the CAPEv2 malware sandbox, allowing the capture of traces of static, dynamic, and network behavior features. The ransomware features have shown significant activity of varying identified functions used in encryption, file application programming interface (API), and network function calls. During the machine learning categorization phase, there are eight identified clusters that have similar and different features regarding function-call sequencing events and file access manipulation for dropping file notes and writing encryption. Having compared all the clusters using a “supervenn” pictorial diagram, the characteristics of the static and dynamic behavior of the ransomware give the initial baselines for comparison with other variants that may have been added to the collected data for intelligence gathering. The findings provide a novel practical approach for intelligence gathering to address ransomware or any other malware variants’ activity patterns to discern similarities, anomalies, and differences between malware actions under study