基于MA及LVQ神经网络的智能NIPS模型与实现

Intelligent NIPS Model and Implementation Based on MA and LVQ Neural Networks

为了提高入侵防御系统的智能性和准确率,在讨论入侵防御技术特性和关键技术的基础上,分析了利用MA(MobileAgent)及LVQ(Learning Vector Quantization)神经网络构建入侵防御系统的优势,以及LVQ神经网络的结构特性和学习算法,提出基于MA及LVQ神经网络的新智能入侵防御系统模型结构,概述了新模型的实现方法,并用Matlab算法进行了仿真实验.结果表明,基于MA及LVQ神经网络的新智能入侵防御系统模型整体防御准确率与检测辨识性能都有较大提高.

To improve the aptitude and accuracy of intrusion prevention system,this paper analyzed the advantages of the intrusion prevention system based on MA and LVQ neural networks,the structural characteristics of LVQ neural networks and learning algorithms on the basis of intrusion prevention technical characteristics and key technologies discussing.This paper proposed a new intelligent intrusion prevention system model and structure based on MA and LVQ neural networks,presented the implementation method of the new model,done simulation experiments in MATLAB software.The results indicate the total prevention blocking accuracy and detection identification performances of the new intelligent intrusion prevention system model based on MA and LVQ neural networks have been improved greatly.