基于AFSA-BPNN的网络入侵检测模型  

Network Intrusion Detection Model Based on AFSA-BPNN

作　　者：岳小冰[1] 

机构地区：[1]河南工业职业技术学院电子信息工程系

出　　处：《微型电脑应用》2016年第8期30-32,共3页Microcomputer Applications

基　　金：河南省科技计划项目(142102210557);南阳市科技计划项目(KJGG38;KJGG51)

摘　　要：为了提高网络入侵检测的效果,针对BP算法收敛速度慢、易陷入局部极值等难题,提出一种基于人工鱼群算法优化BP神经网络的网络入侵检测模型。该模型在基本BP算法的误差反向传播的基础上,采用人工鱼群算法对BP网络的权值和阀值的调整,不仅充分利用了人工鱼群算法的全局寻优性,同时保持了BP算法的反向传播特点,最后,利用建立网络入侵检测模型。采用KDD CUP 99数据集进行仿真实验,结果表明,模型提高了网络入侵检测正确率,而且执行效率可以满足网络安全实际应用要求。In order to improve the effect of network intrusion detection, a network intrusion detection model based on artificial fish swarm algorithm is proposed to optimize the BP neural network for the problem of slow convergence, falling into local extreme value easily and so on. The model on the foundation of basic BP algorithm of error back-propagation, based on artificial fish swarm algorithm of BP neural network weights and threshold adjustment, not only makes full use of the artificial fish swarm algorithm for global optimization, but also maintains the reverse propagation characteristics of BP algorithm, and finally it is used to establish the network intrusion detection model. Using CUP KDD 99 data set to carry on the simulation experiment, the results show that this model can improve the accuracy of network intrusion detection, and the implementation of efficiency can meet the requirements of the practical application of network security.

关 键 词：入侵检测 神经网络参数 人工鱼群算法 仿真测试 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]