基于图聚类的入侵检测算法  

Intrusion detection method based on graph clustering algorithm

作　　者：王国辉[1] 林果园[1] 

机构地区：[1]中国矿业大学计算机科学与技术学院,江苏徐州221116

出　　处：《计算机应用》2011年第7期1898-1900,共3页journal of Computer Applications

基　　金：江苏省自然科学基金资助项目(BK2007035);中国矿业大学青年科技基金资助项目(0D0610352007A047)

摘　　要：针对当前聚类算法仅依赖于初始聚类中心并且无法精确区别非凹形状类的不足,将图学习知识应用到聚类算法中,提出一种基于图聚类的入侵检测算法P-BFS。为得到较准确的分类模型,算法中引入了一种基于逼近函数的相似性度量方法。实验结果论证了图聚类思想应用于入侵检测系统的优越性;同时表明了,与K-means聚类算法相比,P-BFS图聚类算法具有较高的性能。Concerning the defects of the current clustering algorithm for its dependence only on the initial clustering center and failure in exactly distinguishing classes of non-concave shape,this paper applied the knowledge of group learning into the clustering algorithm and proposed the anomaly intrusion detection algorithm P-BFS based on graph clustering.In order to obtain more correct classification model,this algorithm introduced a measurement method of data points similarity based on the approximate function.The experimental results suggest the advantages of the application of the graph clustering algorithm in the intrusion detection system.In addition,it indicates that compared with the classical K-means clustering algorithm,P-BFS has better performance.

关 键 词：入侵检测 聚类分析 图聚类 逼近函数 聚类熵 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]