文献类型：期刊文献

英文题名：Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks

作者：Zhang, Xueqin[1];Zhou, Yue[1];Pei, Songwen[2];Zhuge, Jingjing[1];Chen, Jiahao[1]

机构：[1]East China Univ Sci & Technol, Dept Elect & Commun Engn, Shanghai 200237, Peoples R China;[2]Univ Shanghai Sci & Technol, Dept Comp Sci & Engn, Shanghai 200093, Peoples R China

年份：2020

卷号：8

起止页码：10989

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200608123030);WOS:【SCI-EXPANDED(收录号:WOS:000525406600025)】；

基金：This work was supported by the Natural Science Foundation of China under Grant NNSFC 61472139.

语种：英文

外文关键词：Network intrusion detection; generative adversarial network; Monte Carlo tree; convolutional neural networks

摘要：Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree search (MCTS) algorithm. The MCTS algorithm enables the generation model to provide a reward value that reflects the probability of generative examples bypassing the detector. To guarantee the antagonism and feasibility of the generative adversarial examples, the bypassing rules are restricted. The experimental results indicate that the missed detection rate of adversarial examples is significantly improved after the MCTS-T generation algorithm. Additionally, we construct a generative adversarial network (GAN) to optimize the detector and improve the detection rate when dealing with adversarial examples. After several epochs of adversarial training, the accuracy of detecting adversarial examples is significantly improved.