A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries

• URL: http://arxiv.org/abs/2111.11487v1
• Date: Mon, 22 Nov 2021 19:26:33 GMT
• Title: A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries
• Authors: Prithviraj Dasgupta and Zachariah Osman
• Abstract summary: We evaluate three recent adversarial malware generation techniques using binary malware samples drawn from a single, publicly available malware data set. Our results show that among the compared techniques, the most effective technique is the one that strategically modifies bytes in a binary's header.
• Score: 2.0559497209595814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of generating adversarial malware by a cyber-attacker where the attacker's task is to strategically modify certain bytes within existing binary malware files, so that the modified files are able to evade a malware detector such as machine learning-based malware classifier. We have evaluated three recent adversarial malware generation techniques using binary malware samples drawn from a single, publicly available malware data set and compared their performances for evading a machine-learning based malware classifier called MalConv. Our results show that among the compared techniques, the most effective technique is the one that strategically modifies bytes in a binary's header. We conclude by discussing the lessons learned and future research directions on the topic of adversarial malware generation.

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