Biblio

Malware is any software that causes harm to the user information, computer systems or network. Modern computing and internet systems are facing increase in malware threats from the internet. It is observed that different malware follows the same patterns in their structure with minimal alterations. The type of threats has evolved, from file-based malware to fileless malware, such kind of threats are also known as Advance Volatile Threat (AVT). Fileless malware is complex and evasive, exploiting pre-installed trusted programs to infiltrate information with its malicious intent. Fileless malware is designed to run in system memory with a very small footprint, leaving no artifacts on physical hard drives. Traditional antivirus signatures and heuristic analysis are unable to detect this kind of malware due to its sophisticated and evasive nature. This paper provides information relating to detection, mitigation and analysis for such kind of threat.

This is very true for the Windows operating system (OS) used by government and private organizations. With Windows, the closed source nature of the operating system has unfortunately meant that hidden security issues are discovered very late and the fixes are not found in real time. There needs to be a reexamination of current static methods of malware detection. This paper presents an integrated system for automated and real-time monitoring and prediction of rootkit and malware threats for the Windows OS. We propose to host the target Windows machines on the widely used Xen hypervisor, and collect process behavior using virtual memory introspection (VMI). The collected data will be analyzed using state of the art machine learning techniques to quickly isolate malicious process behavior and alert system administrators about potential cyber breaches. This research has two focus areas: identifying memory data structures and developing prediction tools to detect malware. The first part of research focuses on identifying memory data structures affected by malware. This includes extracting the kernel data structures with VMI that are frequently targeted by rootkits/malware. The second part of the research will involve development of a prediction tool using machine learning techniques.

Detecting botnets and advanced persistent threats is a major challenge for network administrators. An important component of such malware is the command and control channel, which enables the malware to respond to controller commands. The detection of malware command and control channels could help prevent further malicious activity by cyber criminals using the malware. Detection of malware in network traffic is traditionally carried out by identifying specific patterns in packet payloads. Now bot writers encrypt the command and control payloads, making pattern recognition a less effective form of detection. This paper focuses instead on an effective anomaly based detection technique for bot and advanced persistent threats using a data mining approach combined with applied classification algorithms. After additional tuning, the final test on an unseen dataset, false positive rates of 0% with malware detection rates of 100% were achieved on two examined malware threats, with promising results on a number of other threats.