Machine Learning-Assisted Modification of PE Malware for Antivirus Evasion

Abstract

Due to the popularity of malware used during attacks, malware authors constantly create new ways of obfuscating said malware in order to bypass antivirus solutions. Since nowadays antivirus solutions do not rely solely on signatures but use machine learning classifiers to detect malware this opens a new way of creating evasive malware by creating adversarial examples. This thesis searches an efficient way to create adversarial examples of malware that are able to evade static detection of commercial antivirus machines without breaking the functionality of the malware. For this purpose a threat model was designed in which an attacker wants to evade an antivirus solution without any knowledge about its model. The black-box classifier can be queried by the attacker with a sample for which a binary feedback will be provided that indicates whether the sample was classified as malicious or benign. Based on this information the attacker wants to create adversarial examples with a high chance of evasion in as little time as possible. To do so, several existing approaches were analyzed and evaluated, from which the most promising one was selected for further improvement. The Multi Armed Bandit approach by Song et al. [58] was able to distinguish itself due to its beneficial properties regarding search space complexity, functionality preservation and resistance to noise. A hybrid scan mode was proposed to increase the performance of the existing approach by substituting the antivirus machine in selected cases with a locally run machine learning classifier. Results showed that the modified approach was able to create adversarial examples of malware with a significant performance increase without compromising the evasion rate of the original approach. In addition to the proposed attack, several defense measures to prevent evasion attacks through adversarial malware in general as well as defense measures specifically designed to prevent attacks using the proposed approach have been described. In this thesis it was shown that attacks against the machine learning classifier of antivirus solutions are possible and can be used to create adversarial examples of malware that are able to evade static detection.

Datum der Bewilligung	2025
Originalsprache	Englisch
Betreuer/-in	Eckehard Hermann (Betreuer*in)