现代电子技术2016，Vol.39Issue(6)：10-13,4.DOI:10.16652/j.issn.1004-373x.2016.06.003
基于数据挖掘技术的网络入侵检测技术研究
Research on network intrusion detection technology based on data mining technology
摘要
Abstract
The network intrusion detection technology based on data mining technology is studied in this paper. On account of low detection accuracy and efficiency of the network intrusion detection technology established by the common BP natural net⁃work which is easy to fall into least value,the particle swarm algorithm is used to optimize the BP natural network model,the dynamical inertia weight coefficient is adopted to define the parameters of BP natural network,and the parameter of BP neural network are integrated with the characteristics of network intrusion rate,and encoded to a particle in order to realize the synchro⁃nous selection of the characteristics of network intrusion rate and parameter of BP neural network. The detection model estab⁃lished with this method and the common BP natural network are trained and tested by using the intrusion flow data in CUP99 KDD database. The results show that the detection model established with this algorithm has advantages of high detection effi⁃ciency and accuracy.关键词
数据挖掘/BP神经网络/网络入侵检测/粒子群优化算法Key words
data mining/BP neural network/network intrusion detection/particle swarm optimization algorithm分类
信息技术与安全科学引用本文复制引用
周立军,张杰,吕海燕..基于数据挖掘技术的网络入侵检测技术研究[J].现代电子技术,2016,39(6):10-13,4.基金项目
海军航空工程学院基础研究基金基于snort的网络入侵检测技术研究 （）
