A Poisoning Attack on Intrusion Detection System Based on SVM
QIAN Ya-guan1, LU Hong-bo1, JI Shou-ling2, ZHOU Wu-jie3, WU Shu-hui1, LEI Jing-sheng1, TAO Xiang-xing1
1. School of Science & Big Data Science, Zhejiang University of Science and Technology, Hangzhou, Zhejiang 310023, China;
2. College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310058, China;
3. School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang 310023, China
Abstract：Machine learning is widely applied in various intelligent devices including intrusion detection systems (IDS).We propose a novel approach called poising attack on IDS based on SVM.This attack is to degrade detection rate of IDS by misleading the SVM learning process with poisoned training data set.We model the poisoning attack as an optimization problem and solve it with numerical approach to get poisoned data set.At last,NSL-KDD data including several real attacks is used in our experiments,and two measures of precision and callback are used to evaluate the effectiveness.The result shows the poisoning attack approach can significantly degrade the IDS performance.This study may further understand the possible new attacks on machine learning,and provide the basis for the next study of the corresponding defense methods.
钱亚冠, 卢红波, 纪守领, 周武杰, 吴淑慧, 雷景生, 陶祥兴. 一种针对基于SVM入侵检测系统的毒性攻击方法[J]. 电子学报, 2019, 47(1): 59-65.
QIAN Ya-guan, LU Hong-bo, JI Shou-ling, ZHOU Wu-jie, WU Shu-hui, LEI Jing-sheng, TAO Xiang-xing. A Poisoning Attack on Intrusion Detection System Based on SVM. Acta Electronica Sinica, 2019, 47(1): 59-65.
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2019, Vol. 47 
