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....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 generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose 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.展开更多
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....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 generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose 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.展开更多
基金Chinese National Key Laboratory of Science and Technology on Information System Security and National Natural Science Foundation of China under Grant No.U1836105The research of R.J.Rodríguez and X.Chang has been supported in part by the University of Zaragoza and the Fundación Ibercaja under Grant JIUZ-2020-TIC-08The research of R.J.Rodríguez has also been supported in part by the University,Industry and Innovation Department of the Aragonese Government under Programa de Proyectos Estratégicos de Grupos de Investigación(DisCo research group,ref.T21-20R).
文摘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 generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose 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.
基金The research of J.Wang,X.Chang,Y.Wang and J.Zhang was supported in part by Project supported by Chinese National Key Laboratory of Science and Technology on Information System Security and National Natural Science Foundation of China under Grant No.U1836105The research of R.J.Rodriguez and X.Chang has been supported in part by the University of Zaragoza and the Fundacion Ibercaja under Grant JIUZ-2020-TIC-08The research of R.J.Rodriguez has also been supported in part by the University,Industry and Innovation Department of the Aragonese Government under Programa de Proyectos Estrategicos de Grupos de Investigacidn(DisCo research group,ref.T21-20R).
文摘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 generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose 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.
