对抗训练驱动的恶意代码检测增强方法

To solve the deficiency of the malicious code detector’s ability to detect adversarial input, an adversarial training driven malicious code detection enhancement method was proposed. Firstly, the applications were preprocessed by a decompiler tool to extract API call features and map them into binary feature vectors. Secondly, the Wasserstein generative adversarial network was introduced to build a benign sample library to provide a richer combination of perturbations for malicious sample evasion detectors. Then, a perturbation reduction algorithm based on logarithmic backtracking was proposed. The benign samples were added to the malicious code in the form of perturbations, and the added benign perturbations were culled dichotomously to reduce the number of perturbations with fewer queries. Finally, the adversarial malicious code samples were marked as malicious and the detector was retrained to improve its accuracy and robustness of the detector. The experimental results show that the generated malicious code adversarial samples can evade the detector well. Additionally, the adversarial training increases the target detector’s accuracy and robustness.