文献类型：期刊文献

中文题名：混合粒子群优化算法选择特征的网络入侵检测

英文题名：Detection of Network Intrusion Based on Hybrid Particle Swarm Optimization Algorithm Selection Features

作者：袁开银[1];费岚[1]

第一作者：袁开银

机构：[1]河南财经政法大学现代教育技术中心

第一机构：河南财经政法大学

年份：2016

卷号：54

期号：2

起止页码：309-314

中文期刊名：吉林大学学报：理学版

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家自然科学基金(批准号:60972082)

语种：中文

中文关键词：互联网络;选择特征;入侵检测模型;分类器;混合粒子群优化算法

外文关键词：internet; selection feature; intrusion detection model; classifier; hybrid particle swarm optimization algorithm

摘要：针对网络入侵特征优化问题,提出一种混合粒子群优化算法选择特征的网络入侵检测模型,以提高网络入侵检测率.首先将网络入侵检测率作为特征选择的目标函数,网络状态特征作为约束条件建立相应的数学模型,然后采用混合粒子群算法找到最优特征子集,最后采用支持向量机作为分类器建立入侵检测模型,并在MATLAB2012平台上采用KDD1999数据进行验证.实验结果表明,该模型可高效地查询到最优特征子集,入侵检测率和效率均优于经典入侵检测模型.
Aiming at the problem of network intrusion feature optimization,we proposed a network intrusion detection model based on hybrid particle swarm optimization algorithm selecting features to improve the network intrusion detection rate. Firstly,we took the detection rate of network intrusion as the objective function for feature selection,and the network state features as the constraint conditions to establish the corresponding mathematical model. Secondly,we used the hybrid particle swarm optimization algorithm to find the optimal feature subset. Finally,we took support vector machine as classifier to build intrusion detection model,and carried out the verification experiment by using KDD1999 data on MATLAB2012 platform. The results show that the model can efficiently query the optimal features subset,and intrusion detection rate and efficiency are better than the classical intrusion detection model.