Kernel-based adversarial attacks and defenses on support vector classification  

作　　者：Wanman Li Xiaozhang Liu Anli Yan Jie Yang 

机构地区：[1]School of Computer Science and Technology,Hainan University,Haikou,570228,China

出　　处：《Digital Communications and Networks》2022年第4期492-497,共6页数字通信与网络（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant No.61966011.

摘　　要：While malicious samples are widely found in many application fields of machine learning,suitable countermeasures have been investigated in the field of adversarial machine learning.Due to the importance and popularity of Support Vector Machines(SVMs),we first describe the evasion attack against SVM classification and then propose a defense strategy in this paper.The evasion attack utilizes the classification surface of SVM to iteratively find the minimal perturbations that mislead the nonlinear classifier.Specially,we propose what is called a vulnerability function to measure the vulnerability of the SVM classifiers.Utilizing this vulnerability function,we put forward an effective defense strategy based on the kernel optimization of SVMs with Gaussian kernel against the evasion attack.Our defense method is verified to be very effective on the benchmark datasets,and the SVM classifier becomes more robust after using our kernel optimization scheme.

关 键 词：Adversarial machine learning Support vector machines Evasion attack Vulnerability function Kernel optimization 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]