A Comparison of Adversarial Learning Techniques for Malware Detection
Pavla Louthánová Matouš Kozák Martin Jureček Mark Stamp
Machine learning has proven to be a useful tool for automated malware
detection, but machine learning models have also been shown to be vulnerable to
adversarial attacks. This article addresses the problem of generating
adversarial malware samples, specifically malicious Windows Portable Executable
files. We summarize and compare work that has focused on adversarial machine
learning for malware detection. We use gradient-based, evolutionary
algorithm-based, and reinforcement-based methods to generate adversarial
samples, and then test the generated samples against selected antivirus
products. We compare the selected methods in terms of accuracy and practical
applicability. The results show that applying optimized modifications to
previously detected malware can lead to incorrect classification of the file as
benign. It is also known that generated malware samples can be successfully
used against detection models other than those used to generate them and that
using combinations of generators can create new samples that evade detection.
Experiments show that the Gym-malware generator, which uses a reinforcement
learning approach, has the greatest practical potential. This generator
achieved an average sample generation time of 5.73 seconds and the highest
average evasion rate of 44.11%. Using the Gym-malware generator in combination
with itself improved the evasion rate to 58.35%.