一种改进的基于神经网络的入侵检测算法  

An Improved Intrusion Detection Algorithm Based on Neural Network

作　　者：杨秀英[1] 

机构地区：[1]南京铁道职业技术学院,南京210031

出　　处：《科技通报》2013年第2期197-199,共3页Bulletin of Science and Technology

基　　金：上海铁路局"通信信号装备现代化关键技术-电务标准化作业监控系统研究项目"(2010059)

摘　　要：径向基神经网络具有良好的分类特性,被广泛应用于入侵检测系统的研究中,然而RBF神经网络需要事先确定隐层神经元数目,并且容易陷入局部最优。利用遗传算法优化隐层神经元数目,并且基于粒子群思想优化隐层到输出层的权值,同时给出了详细的算法流程。经Lincoln实验室入侵检测系统数据评估集合测试,该智能算法的检测成功率大大提高,并且训练时间比较短,完全可以应用于入侵检测系统中。Radial basis neural network has good classification performance,and is widely usedin the research of network intrusion detection system,however the RBF neural network needs a predetermined number of neurons in hidden layer,and is easy to fall into local optimum.An improved algorithm is worked out,which use genetic algorithm to optimize the number of the hidden neurons.and use Particle Swarm algorithm hidden layer to the output layer weights.A detailed algorithm is gived.This algorithm is tested based on The Lincoln laboratory intrusion detection system data evaluation set.The result shows that this algorithm has greatly improved the success rate,and has short training time.It can be applied to intrusion detection system.

关 键 词：径向基神经网络 遗传算法 粒子群 权值优化 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]