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粒子群选择特征和信息增益确定特征权值的入侵检测
引用本文:黄会群,孙 虹.粒子群选择特征和信息增益确定特征权值的入侵检测[J].计算机应用,2014,34(6):1686-1688.
作者姓名:黄会群  孙 虹
作者单位:1. 湖南财政经济学院 信息管理系, 长沙 410205 2. 中南大学 公共卫生学院, 长沙 410078;
摘    要:为了提高网络入侵检测正确率,提出一种粒子群算法(PSO)选择特征和信息增益(IG)法确定特征权值的网络入侵检测模型(PSO-IG)。首先采用PSO选择网络入侵特征子集,消除冗余特征;然后采用IG法确定特征子集中的特征权重,并采用支持向量机(SVM)建立分类模型;最后采用KDD CUP 99 数据集对PSO-IG的性能进行测试。测试结果表明:PSO-IG消除了冗余特征,降低了输入维数,提高了网络入侵检测速度;通过合理确定特征权值,提高了入侵检测正确率。

关 键 词:特征选择  特征权值  入侵检测  粒子群算法
收稿时间:2013-12-13
修稿时间:2014-01-25

Network intrusion detection based on particle swarm optimization algorithm and information gain
HUANG Huiqun SUN Hong.Network intrusion detection based on particle swarm optimization algorithm and information gain[J].journal of Computer Applications,2014,34(6):1686-1688.
Authors:HUANG Huiqun SUN Hong
Affiliation:1. Department of Information Management, Hunan University of Finance and Economics, Changsha Hunan 410205, China
2. School of Public Health, Central South University, Changsha Hunan 410078, China;
Abstract:In order to improve the detection accuracy of network intrusion, a network intrusion detection model named PSO-IG was proposed based on Particle Swarm Optimization (PSO) algorithm and Information Gain (IG). Firstly, PSO algorithm was used to eliminate redundant features of original network data, and then the weight values of selection features were obtained using IG, and Support Vector Machine (SVM) was used to establish intrusion detection model. Finally, the KDD CUP 99 dataset was used to test the performance of PSO-IG. The results show that the proposed model can eliminate redundant features and reduce the input dimension to improve the detection speed of network intrusion, and it can improve the network intrusion detection accuracy by reasonable selecting weight values.
Keywords:
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