基于数据挖掘算法的网络入侵检测系统研究  

The Research of Network Intrusion Detection System Based on Algorithm of Data Mining

作　　者：岳耀雪[1] 

机构地区：[1]山东理工职业学院,山东济宁272017

出　　处：《计算机安全》2009年第10期41-43,共3页Network & Computer Security

摘　　要：回顾了当前入侵检测技术和数据挖掘技术,分析了Snort网络入侵检测系统存在的问题,重点研究了数据挖掘中的关联算法Apriori算法和聚类算法K一均值算法;在Snort入侵检测系统的基础上,增加了正常行为挖掘模块、异常检测模块和新规则生成模块,构建了基于数据挖掘技术的网络入侵检测系统模型。新模型能够有效地检测新的入侵行为,而且提高了系统的检测效率。The article reviews intrusion detection and data mining techniques detection system, especially module, anomaly detection system model based on data researches the association rule algorithm Apriori and , analyses the clustering rule engine module and new rules generating module are added to the existing problems o algorithm K-means Snort NIDS, finally, f the Snort network intrusion normal behavior patterns mining designs an intrusion detection mining technique. The new system can not only effectively detect new invasion, but also promote detective speed.

关 键 词：入侵检测系统 数据挖掘 聚类分析 关联分析 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP311.13[自动化与计算机技术—计算机科学与技术]