基于数据挖掘的网络入侵检测模型  

Research on Network Intrusion Detection Model Based on Data Mining

作　　者：唐星科 李捷[2] 蒋延军[3] 

机构地区：[1]广西桂林林业学校,广西桂林541004 [2]桂林陆军学院地方生院,广西桂林541002 [3]昆明理工大学,云南昆明650051

出　　处：《计算机安全》2007年第11期17-18,22,共3页Network & Computer Security

摘　　要：入侵检测技术已经成为网络安全的新兴领域。该文针对入侵检测系统的特点与不足,提出了一种基于数据挖掘算法的网络入侵检测系统模型,能高效地进行误用检测与异常检测,可降低漏报率和误报率,同时应用聚类算法对边界区进行分析,可发现未知攻击,具有很好的实用性。　Intrusion detection already becomes a new network security flied. In this paper, according to characters and shortages of traditional intrusion detection system, a network intrusion detection system model based on data mining algorithm has been presented. It can not only carry thought misuse detection and abnormal detection effectively, but also can reduce failure report ratio and distort ratio. Meanwhile it use clustering algorithm to analyze verge of two detection model, then can discover unknown attack. It is good practicability.

关 键 词：数据挖掘 关联规则 聚类算法 入侵检测 检测模型 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]