Creating Valid Adversarial Examples of Malware

• URL: http://arxiv.org/abs/2306.13587v1
• Date: Fri, 23 Jun 2023 16:17:45 GMT
• Title: Creating Valid Adversarial Examples of Malware
• Authors: Matou\v{s} Koz\'ak, Martin Jure\v{c}ek, Mark Stamp, Fabio Di Troia
• Abstract summary: We present a generator of adversarial malware examples using reinforcement learning algorithms. Using the PPO algorithm, we achieved an evasion rate of 53.84% against the gradient-boosted decision tree (GBDT) model. random application of our functionality-preserving portable executable modifications successfully evades leading antivirus engines.
• Score: 4.817429789586127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results. As a result, antivirus developers are incorporating machine learning models into their products. While these models improve malware detection capabilities, they also carry the disadvantage of being susceptible to adversarial attacks. Although this vulnerability has been demonstrated for many models in white-box settings, a black-box attack is more applicable in practice for the domain of malware detection. We present a generator of adversarial malware examples using reinforcement learning algorithms. The reinforcement learning agents utilize a set of functionality-preserving modifications, thus creating valid adversarial examples. Using the proximal policy optimization (PPO) algorithm, we achieved an evasion rate of 53.84% against the gradient-boosted decision tree (GBDT) model. The PPO agent previously trained against the GBDT classifier scored an evasion rate of 11.41% against the neural network-based classifier MalConv and an average evasion rate of 2.31% against top antivirus programs. Furthermore, we discovered that random application of our functionality-preserving portable executable modifications successfully evades leading antivirus engines, with an average evasion rate of 11.65%. These findings indicate that machine learning-based models used in malware detection systems are vulnerable to adversarial attacks and that better safeguards need to be taken to protect these systems.

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