Creating valid adversarial examples of malware

Abstract Because of its world-class results, machine learning (ML) is becoming increasingly popular as a go-to solution for many tasks. As a result, antivirus developers are incorporating ML models into their toolchains. 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 scenario is more applicable in practice for the domain of malware detection. We present a method of creating 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) detector. 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 ML-based models used in malware detection systems are sensitive to adversarial attacks and that better safeguards need to be taken to protect these systems.