一种基于数据挖掘的Snort系统的设计与应用  

Design and Application of a Snort System Based on Data Mining

作　　者：魏德志[1] 王奇光[1] 林丽娜[1] 

机构地区：[1]集美大学诚毅学院,福建厦门361021

出　　处：《集美大学学报（自然科学版）》2011年第5期397-400,共4页Journal of Jimei University：Natural Science

基　　金：福建省仿脑智能系统重点实验室开放课题项目(BLISSOS2010103)

摘　　要：为了提高入侵的检测效率,提出了一种基于数据挖掘的改进的Snort系统.该系统充分利用数据挖掘的入侵检测优点,采用改进的Apriori算法,在Snort原系统基础上增加一个数据异常检测模块,改进了Snort存在的缺点,提高了检测率.通过模拟实验验证和实际网络环境应用分析,得出该系统比原Snort系统具有更高的检测性能,能检测未知的网络入侵,提高计算机系统的安全性.In order to improve the efficiency of intrusion detection,the paper proposed an improved Snort system based on data mining.The system took advantage of the data mining in intrusion detection,and emplayed the improved Apriori algorithm.A data was added anomaly detection module to the Snort system,amended the fault of Snort,and improved the rate of detection.The simulation results and the actual application of the network environment showed that the improved system had a higher performance than the original Snort,and could detect unknown intrusion and enhance the security of computer systems.

关 键 词：数据挖掘 入侵检测 APRIORI算法 SNORT 

分 类 号：TP393.0[自动化与计算机技术—计算机应用技术]