改进支持向量机在网络入侵检测中的应用

Application of Improved Support Vector Machines in Network Intrusion Detection

为提高网络安全性,提出一种改进支持向量机的网络入侵检测算法.首先采用核主成分分析提取网络数据重要特征,加快网络入侵检测速度,然后采用粒子群算法对支持向量机参数进行优化,提高网络检测正确率.仿真实验结果表明,改进支持向量提高网络入侵检测正确率,降低漏检率,同时加快了网络入侵检测速度,是一种有效、实时性较强的网络入侵检测算法.

In order to improve the security of the network,this paper proposes an improved support vector machine algorithm for network intrusion detection.Firstly,kernel principal component analysis is used to extract data network feature,accelerate the network intrusion detection rate,then the particle swarm optimization algorithm is used to optimize the support vector machine parameters,improve the network detection accuracy rate.The simulation results show that,the proposed method has improved network intrusion detection rate,reduce the failure rate,and speed the network intrusion detection,it is an efficient,real-time network intrusion detection algorithm.