Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection
This work addresses the challenge of improving evasion techniques for malware detection systems, which is incremental as it builds on existing methods with a novel approach.
The authors tackled the problem of evading graph neural network-based malware detection by proposing a reinforcement learning attack that inserts semantic Nops to preserve functionality, achieving a significantly higher evasion rate than three baseline attacks.
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.