  As an increasing number of deep-learning-based malware scanners have been
proposed, the existing evasion techniques, including code obfuscation and
polymorphic malware, are found to be less effective. In this work, we propose a
reinforcement learning-based semantics-preserving
(i.e.functionality-preserving) attack against black-box GNNs (GraphNeural
Networks) for malware detection. The key factor of adversarial malware
generation via semantic Nops insertion is to select the appropriate
semanticNopsand their corresponding basic blocks. The proposed attack uses
reinforcement 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 Windows
malware samples and various benign Windows programs. The evaluation results
have shown that the proposed attack can achieve a significantly higher evasion
rate than three baseline attacks, namely the semantics-preserving random
instruction insertion attack, the semantics-preserving accumulative instruction
insertion attack, and the semantics-preserving gradient-based instruction
insertion attack.
