网络入侵检测系统中的频繁模式挖掘  

Mining Frequent Patterns in Network Intrusion Detection

作　　者：赵观军[1] 丁宏[1] 王泽[1] 

机构地区：[1]杭州电子科技大学计算机学院,浙江杭州310018

出　　处：《计算机应用研究》2006年第6期121-123,共3页Application Research of Computers

基　　金：浙江省自然科学基金资助项目(Y104426);浙江省教育厅高校科研计划资助项目(20040457)

摘　　要：为了解决网络入侵检测领域使用Apriori算法挖掘频繁模式效率不高、精度不够的问题,在FP-growth的基础上提出一种新的基于分割原理的PFP-growth算法。该算法采用分而治之的方法,既有效利用了FP-tree特性,又减轻了系统挖掘大容量数据库的负荷,使挖掘效率有了明显提高。另外设计了一种新的最小支持度设置法,使挖掘的频繁模式更精确。In the network intrusion detection, Apriori algorithm is used to extract relative rules, but its processing precision and efficiency are not satisfactory. In order to resolve the problem, based on FP-growth, this paper proposes a new algorithm named PFP-growth, this algorithm applies an idea of divide and rule, makes good use of FP-tree, and eases the load of system when mining a large database, which makes its velocity improved obviously. Besides we design a new method to set min-support, which makes frequent patterns mined much precise.

关 键 词：入侵检测 关联规则 频繁模式 APRIORI FP—growth 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]