Publication Date
1-1-2024
Document Type
Article
Publication Title
Journal of Computer Virology and Hacking Techniques
DOI
10.1007/s11416-024-00519-z
Abstract
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.
Funding Number
CZ.02.1.01/0.0/0.0/16 019/0000765
Funding Sponsor
Ministerstvo Školství, Mládeže a Tělovýchovy
Keywords
Adversarial malware, Machine learning, Malware detection, PE files
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Science
Recommended Citation
Pavla Louthánová, Matouš Kozák, Martin Jureček, Mark Stamp, and Fabio Di Troia. "A comparison of adversarial malware generators" Journal of Computer Virology and Hacking Techniques (2024). https://doi.org/10.1007/s11416-024-00519-z
