通信学报2018，Vol.39Issue(12)：60-68,9.DOI:10.11959/j.issn.1000-436x.2018287
改进粒子群联合禁忌搜索的特征选择算法
Feature selection algorithm based on improved particle swarm joint taboo search
摘要
Abstract
To solve the problem of high data feature dimensionality in intrusion detection, a feature selection algorithm based on improved particle swarm optimization taboo search (IPSO-TS) was proposed. The genetic algorithm was used to improve the particle swarm optimization, and the initial optimal solution of feature selection was obtained. A taboo search (TS) algorithm was used for initial optimal solution to obtain the global optimal solution of the feature subset. Compared with genetic algorithm integrated particle swarm optimization (CMPSO), particle swarm optimization (PSO) and PSO-TS algorithms, experimental results based on the KDD CUP 99 dataset show that the method reduces the features by about 29.2％, shortens about 15％ of the average detection time, and increases about 2.96％ of the average classification accuracy.关键词
入侵检测/特征选择/粒子群/遗传算法/禁忌搜索Key words
intrusion detection/ feature selection/ particle swarm optimization/ genetic algorithm/ taboo search分类
信息技术与安全科学引用本文复制引用
张震,魏鹏,李玉峰,兰巨龙,徐萍,陈博..改进粒子群联合禁忌搜索的特征选择算法[J].通信学报,2018,39(12):60-68,9.基金项目
国家重点研究发展计划基金资助项目(No.2017YFB0803201) （No.2017YFB0803201）
国家自然科学基金资助项目(No.61502528) （No.61502528）
网络空间安全专项课题基金资助项目(No.2017YFB0803204) （No.2017YFB0803204）
上海市科学技术委员会科研计划课题基金资助项目(No.16DZ1120503) （No.16DZ1120503）
