摘要
针对常规网络入侵检测算法检测率低、误报率高以及检测效率低下等问题,在此使用基于混合核函数的最小二乘支持向量机作为网络入侵检测模型的核心算法,使用粒子群优化算法对最小二乘支持向量机的各个参数进行优化。使用著名的KDD CUP99数据库中的部分数据样本对网络入侵检测模型进行训练和测试,以验证所提出网络入侵检测方法的性能。测试实验结果表明,提出的基于混合核函数的PSO-LSSVM算法具有更好的检测性能,提高了检测系统的检测率。
Since the conventional detection algorithm of network intrusion has low detection rate,high false positive rate and low detection efficiency,the least squares support vector machine（LSSVM）algorithm based on hybrid kernel function is taken as the core algorithm of the network intrusion detection model,and each parameter of the LSSVM is optimized by using particle swarm optimization（PSO）algorithm. The network intrusion detection model was trained and tested by partial data samples in famous KDD CUP99 database to verify the performance of the proposed network intrusion detection method. The test results show that the PSO-LSSVM algorithm based on hybrid kernel function has better detection performance,and can improve the detection rate of the detection system.
出处
《现代电子技术》
北大核心
2015年第21期96-99,共4页
Modern Electronics Technique
基金
咸阳师范学院专项科研计划项目:基于人工智能的三维油藏数据处理研究(07XSYK224)
陕西省教育厅专项科研计划项目:信息化环境下关中方言的保护与传承(12JK0212)
陕西省教育厅科研项目(2013JK0524)研究成果
