关键词: 协同量子粒子群算法, 最小二乘支持向量机, 特征选择, 网络入侵检测

Abstract: In order to improve the detection rate of network intrusion, a novel network intrusion detection model is proposed in this paper based on cooperative quantum-behaved particle swarm optimization algorithm and least square support vector machine. The feature subset is coded as the position of particle, and the detection rate is taken as evaluation criteria of the feature subset, and the cooperative quantum-behaved particle swarm optimization algorithm is used to find the optimal feature subset, the intrusion detection model is built based on the optimal feature subset by least square support vector machine, the simulation experiment is carried out on the KDD CUP 99 data. The results show that, compared with other models, the proposed algorithm has improved detection efficiency and the detection rate of the network intrusion.

Key words: cooperative quantum-behaved particle swarm optimization algorithm, least square support vector machine, feature selection, network intrusion detection