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As an increasing number of deep-learning-based malware scanners have beenproposed, the existing evasion techniques, including code obfuscation andpolymorphic malware, are found to be less effective. In this work, we propose areinforcement learning-based semantics-preserving(i.e.functionality-preserving) attack against black-box GNNs (GraphNeuralNetworks) for malware detection. The key factor of adversarial malwaregeneration via semantic Nops insertion is to select the appropriatesemanticNopsand their corresponding basic blocks. The proposed attack usesreinforcement learning to automatically make these "how to select" decisions.To evaluate the attack, we have trained two kinds of GNNs with five types(i.e.,Backdoor, Trojan-Downloader, Trojan-Ransom, Adware, and Worm) of Windowsmalware samples and various benign Windows programs. The evaluation resultshave shown that the proposed attack can achieve a significantly higher evasionrate than three baseline attacks, namely the semantics-preserving randominstruction insertion attack, the semantics-preserving accumulative instructioninsertion attack, and the semantics-preserving gradient-based instructioninsertion attack.

Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection