LSGAN-AT:enhancing malware detector robustness against adversarial examples

• 作 者: Jianhua Wang Xiaolin Chang Yixiang Wang Ricardo J.Rodríguez Jianan Zhang
• 机 构: Beijing Key Laboratory of Security and Privacy in Intelligent Transportation,Beijing Jiaotong Univer
• 出 处: 网络空间安全科学与技术（英文版）
• 发表日期: 2022年1期
• 关键词: Adversarial malware example Generative adversarial network Machine learning Malw

Adversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement.Generative Adversarial Network (GAN) is a kind of AME generation method,but the existing GAN-based AME genera-tion methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we pro-pose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples,which includes LSGAN module and AT module.LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square (LS) loss to optimize boundary samples.AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector (RMD).Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack.The results also verify the performance of the generated RMD in the recognition rate of AME.