Combining Generators of Adversarial Malware Examples to Increase Evasion Rate

Kozák, Matouš; Jureček, Martin

doi:10.5220/0012127700003555

Cited by 1 publication

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“…In the last experiment, we test the effectiveness of using a combination of methods to generate malware samples [40]. The goal of this experiment is to test whether using multiple adversarial example generators per malware sample would significantly increase the malware evasion rate.…”

Section: Combination Of Multiple Techniquesmentioning

confidence: 99%

A comparison of adversarial malware generators

Louthánová,

Kozák,

Jureček

et al. 2024

J Comput Virol Hack Tech

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Machine learning has proven to be a valuable tool for automated malware detection, but machine learning systems have also been shown to be subject to adversarial attacks. This paper summarizes and compares related work on generating adversarial malware samples, specifically malicious Windows Portable Executable files. In contrast with previous research, we not only compare generators of adversarial malware examples theoretically, but we also provide an experimental comparison and evaluation for practical usability. We use gradient-based, evolutionary-based, and reinforcement-based approaches to create adversarial samples, which we test against selected antivirus products. The results show that applying optimized modifications to previously detected malware can lead to incorrect classification of the file as benign. Moreover, generated malicious samples can be effectively employed against detection models other than those used to produce them, and combinations of methods can construct new instances that avoid detection. Based on our findings, the Gym-malware generator, which uses reinforcement learning, has the greatest practical potential. This generator has the fastest average sample production time of 5.73 s and the highest average evasion rate of 44.11%. Using the Gym-malware generator in combination with itself further improved the evasion rate to 58.35%. However, other tested methods scored significantly lower in our experiments than reported in the original publications, highlighting the importance of a standardized evaluation environment.

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Section: Combination Of Multiple Techniquesmentioning

confidence: 99%