基于混沌猴群优化的LSSVM网络入侵检测模型建构  

LSSVM Network Intrusion Detection Model Based on Chaos Optimization of Monkey Swarm Algorithm

作　　者：符保龙[1] Fu Baolong(Liuzhou Vocational&Technical College,Liuzhou Guangxi 545006,China)

机构地区：[1]柳州职业技术学院,广西柳州545006

出　　处：《柳州职业技术学院学报》2021年第1期89-93,共5页Journal of Liuzhou Vocational & Technical College

基　　金：柳州职业技术学院科研项目(2017KA05)。

摘　　要：针对网络入侵数据具有高维度、数据量大的特点,引入混沌算子完成标准猴群算法的初始化操作,接着利用优化后的猴群算法对LSSVM的核函数系数以及函数的调节系数进行优化,构建一个CMA-LSSVM入侵检测模型。仿真实验结果表明,该模型对网络入侵数据具有较好的泛化能力和较高的检测精度,效果良好。In view of the high dimensionality and large amount of data of network intrusion data,chaos operator is introduced to complete the initialization operation of the standard monkey swarm algorithm,and then the optimized monkey swarm algorithm is used to optimize the kernel function coefficients of LSSVM and the adjustment coefficient of the function,a CMA-LSSVM intrusion detection model is constructed.The simulation experiment results show that the model has good generalization ability and high detection accuracy for network intrusion data,and the effect is good.

关 键 词：LSSVM算法 混沌优化 猴群算法 入侵检测 

分 类 号：TP309.5[自动化与计算机技术—计算机系统结构]