摘要
针对入侵检测中数据特征维度高的问题,提出了改进粒子群联合禁忌搜索(IPSO-TS)的特征选择算法。采用遗传算子对粒子群算法进行了改进,得到了特征选择初始最优解;对该解进行禁忌搜索(TS)得到了特征子集的全局优化解。基于KDD CUP 99数据集的实验结果表明,相较遗传算子整合粒子群算法(CMPSO)、粒子群算法(PSO)和粒子群联合禁忌算法,IPSO-TS减少了至少29.2%的特征,缩短了至少15%的平均检测时间,提高了至少2.96%的平均分类准确率。
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.
作者
张震
魏鹏
李玉峰
兰巨龙
徐萍
陈博
ZHANG Zhen;WEI Peng;LI Yufeng;LAN Julong;XU Ping;CHEN Bo(National Digital Switching System Engineering and Technological Research and Development Center,Zhengzhou 450002,China;Information Engineering University,Zhengzhou 450002,China)
出处
《通信学报》
EI
CSCD
北大核心
2018年第12期60-68,共9页
Journal on Communications
基金
国家重点研究发展计划基金资助项目(No.2017YFB0803201)
国家自然科学基金资助项目(No.61502528)
网络空间安全专项课题基金资助项目(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)~~
关键词
入侵检测
特征选择
粒子群
遗传算法
禁忌搜索
intrusion detection
feature selection
particle swarm optimization
genetic algorithm
taboo search