计算机工程与应用Issue(18)：69-72,115,5.DOI:10.3778/j.issn.1002-8331.1304-0309
改进数据挖掘算法在入侵检测系统中的应用
Application and realization of improved data mining algorithm in intrusion detection system
摘要
Abstract
Aiming to the existing problem of the powerless, high false negative rate, low detection efficiency and the lack of the rule base automatic extension mechanism to unknown aggressive behavior for existing detection mechanisms, combining the rel-evant knowledge of data mining technology, this paper designs one improved network intrusion detection system model based on data mining, combining misuse detection and anomaly detection. The model selects the K-means algorithm in clustering analysis and the Apriori algorithm in association rule mining and improves it. It applies the improved K-means algorithm to achieving normal behavior classes and data separation module, then utilizes the improved Apriori algorithm to achieve automatic extension of the rule base. By the experiment it verifies the function of the two algorithms.关键词
数据挖掘/入侵检测/改进/K-means算法/Apriori算法Key words
data mining/intrusion detection/improved/K-means algorithm/Apriori algorithm分类
信息技术与安全科学引用本文复制引用
赵艳君,魏明军..改进数据挖掘算法在入侵检测系统中的应用[J].计算机工程与应用,2013,(18):69-72,115,5.基金项目
河北省自然科学基金（No.F2012209019）。 （）
