基于CPSO-LSSVM的网络入侵检测

Network Intrusion Detection Based on CPSO-LSSVM

为提高网络入侵检测效果,提出一种结合混沌粒子群优化(CPSO)算法和最小二乘支持向量机(LSSVM)的网络入侵检测模型。将网络特征和LSSVM参数编码成二进制粒子,根据网络入侵检测正确率和特征子集维数权值构造粒子群目标函数。通过粒子群找到最优特征子集和LSSVM参数,同时引入混沌机制保证粒子群的多样性,防止早熟现象的出现,从而建立最优网络入侵检测模型。采用KDD99数据集进行性能测试,结果表明,该模型不仅能获得最优特征子集和LSSVM参数,而且提高了入侵检测速度和正确率,降低了入侵检测误报率和漏报率。

In order to improve the network intrusion detection effect, this paper puts forward a network intrusion detection model based on Chaotic Particle Swarm Optimization（CPSO） algorithm and Least Squares Support Vector Machine（LSSVM）. The network features and parameters of LSSVM are encoded into binary particles. The objective function of particle swarm optimization algorithm is built based on network intrusion detection accuracy and the dimensions of the feature subset. The particle swarm is used to find the optimal feature subset and LSSVM parameters, while the chaotic mechanism is introduced to guarantee the diversity of particle swarm and to prevent producing precocious phenomenon, the optimal model of the network intrusion detection is established. The performance of proposed model is test by KDD99 data and the simulation results show that proposed model can select the optimal feature subset and LSSVM parameters, the detecting speed and network intrusion detection accuracy are improved, and thereby network intrusion detection false negative rate and false positive rate are reduced.