Anti-forensic = Suspicious: Detection of Stealthy Malware that Hides Its Network Traffic

Abstract

Stealthy malware hides its presence from the users of a system by hooking the relevant libraries, drivers, system calls or manipulating the services commonly used to monitor system behaviour. Tampering the network sensors of host-based intrusion detection systems (HIDS) may impair their ability to detect malware and significantly hinders subsequent forensic investigations. Nevertheless, the mere attempt to hide the traffic indicates malicious intentions. In this paper we show how comparison of the data collected by multiple sensors at different levels of resilience may reveal these intentions. At the lowest level of resilience, information from untrusted sensors such as netstat and process lists are used. At the highest resilience level, we analyse mirrored traffic using a secured hardware device. This technique can be considered as fully trusted. The detection of a discrepancy between what is reported by these common tools and what is observed on a trusted system operating at a different level is a good way to force a dilemma on malware writers: either apply hiding techniques, with the risk that the discrepancy is detected, or keep the status of network connections untouched, with a greater ability for the administrator to recognize the presence and to understand the behaviour of malware. The proposed method was implemented on an evaluation testbed and is able to detect stealthy malware that hides its communication from the HIDS. The false positive rate is 0.01% of the total traffic analysed, and barring a few exceptions that can easily be white-listed, there are no legitimate processes which raise false alerts.

Systematic Review: Anti-Forensic Computer Techniques

“…Anti-forensics and Malware 7 [19,[215][216][217][218][219][220] Authors such us I. You et al [215] discuss the anti-forensic aspect of obfuscation as applied to malware.…”

“…M. Agarwal et al [218] propose a method of detecting the anti-forensic technique of tampering with network sensors of host-based intrusion detection systems (HIDS).…”

“…Among the contributions of these studies are the analysis of anti-forensic techniques directly related to malware [216], the development of process models to handle fileless malware attacks [19], the detection of malware in memory using machine learning [217], the detection of stealthy malware by comparing network sensors [218], and the analysis of malware behavior on Android mobile devices [219].…”

Information Security and Privacy – Challenges and Outlook

“…This requires that all nations have the ability to collect, preserve and examine digital evidence for their own needs as well as for the potential needs of other nations. Digital Forensics is the scientific study of the processes involved in the recovery, preservation and examination of digital evidence, including audio, imaging and communication devices with consideration of forensic evasion techniques [11]. The efforts of the working group in digital forensics strive to discover, define and foster fundamental scientific principles that support the investigation of digital wrongdoings from all perspectives, legal, business and military.…”