华东理工大学学报（自然科学版）2017，Vol.43Issue(5)：662-668,7.DOI:10.14135/j.cnki.1006-3080.2017.05.010
基于改进蝙蝠算法的工业控制系统入侵检测
Intrusion Detection of Industrial Control System Based on Improved Bat Algorithm
李金乐 1王华忠 1陈冬青2
作者信息
- 1. 华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237
- 2. 中国信息安全测评中心,北京100085
- 折叠
摘要
Abstract
Aiming at the local minima problem of the standard bat algorithm (BA),this paper makes two improvements.Firstly,the current local optimal solution distribution is considered during the updating of bats' positions.Secondly,the random variation operation in differential evolution (DE) algorithm is introduced into BA to increase the diversity of the population and enhance the local search ability of the BA algorithm.Besides,the superiority of the proposed algorithm is illustrated by means of typical test functions.Moreover,the proposed algorithm is applied to the parameters optimization of support vector machine (SVM) classifier in industrial control system (ICS) intrusion detection model.The simulation results from the standard dataset for industrial system intrusion detection show that,compared with DE,particle swarm optimization (PSO) and genetic algorithm (GA),the optimized SVM intrusion detection model via the proposed algorithm can effectively improve the detection rate,false negative rate,and false alarm rate.关键词
改进蝙蝠算法/最优解分布/差分进化算法/支持向量机/工业控制系统/入侵检测Key words
improved bat algorithm/optimal solution distribution/DE/SVM/ICS/intrusion detection分类
信息技术与安全科学引用本文复制引用
李金乐,王华忠,陈冬青..基于改进蝙蝠算法的工业控制系统入侵检测[J].华东理工大学学报（自然科学版）,2017,43(5):662-668,7.
