神经网络算法在智能体IDS系统中的应用

Research of Neural Network Algorithm in Agent-Based Intrusion Detection System

结合网络入侵和主机入侵方面的检测能力,构建了基于智能体的分布式入侵检测系统的体系结构模型。重点讨论了神经网络入侵检测算法。针对传统的BP网络在入侵检测应用中学习收敛时间和性能上的不足,提出了变速度回归神经网络(采用了批处理技术和动量方法)检测算法,通过对网络数据集的测试表明,该算法较传统BP网络,其学习训练次数大大降低,学习能力显著提高。

This paper designs an Agent-based Distributed Intrusion Detection System. The DIDS system combines host-based intrusion detection and network-based intrusion detection functions. It can be used to protect large area network and have relatively good expansibility. This paper also discusses the implementation of BP neural network in intrusion detection. Because of the traditional BP NN抯 weakness in learning time and performance, variable learning rate BP regression neural network (batch and momentum techniques are also used) are designed. The algorithm has been tested on a network data set. The result showed that it had much better performance than traditional BP NN.