Network Intrusion Detection Based on CPSO-LSSVM
Mingzhe Liu · Jisuanji gongcheng · 2013
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.