摘要
为了提高网络入侵检测率,提出一种协同量子粒子群算法和最小二乘支持向量机的网络入侵检测模型(CQPSO-LSSVM)。将网络特征子集编码成量子粒子位置,入侵检测正确率作为特征子集优劣的评价标准,采用协同量子粒子群算法找到最优特征子集,采用最小二乘支持向量机建立网络入侵检测模型,并采用KDD CUP 99数据集进行仿真测试。结果表明,CQPSO-LSSVM获得了比其他入侵检测模型更高的检测效率和检测率。
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.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第2期113-116,155,共5页
Computer Engineering and Applications
基金
安徽省"十二五"科技攻关计划项目(No.11010402183)
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
cooperative quantum-behaved particle swarm optimization algorithm
least square support vector machine
feature selection
network intrusion detection
