对抗训练驱动的恶意代码检测增强方法

Adversarial training driven malicious code detection enhancement method

为了解决恶意代码检测器对于对抗性输入检测能力的不足,提出了一种对抗训练驱动的恶意代码检测增强方法。首先,通过反编译工具对应用程序进行预处理,提取应用程序接口(API)调用特征,将其映射为二值特征向量。其次,引入沃瑟斯坦生成对抗网络,构建良性样本库,为恶意样本躲避检测器提供更加丰富的扰动组合。再次,提出了一种基于对数回溯法的扰动删减算法。将良性样本库中的样本以扰动的形式添加到恶意代码中,对添加的扰动进行二分删减,以较少的查询次数减少扰动的数量。最后,将恶意代码对抗样本标记为恶意并对检测器进行重训练,提高检测器的准确性和稳健性。实验结果表明,生成的恶意代码对抗样本可以躲避目标检测器的检测。此外,对抗训练提升了目标检测器的准确率和稳健性。

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.