计算机技术与发展2012，Vol.22Issue(7)：136-139,142,5.
基于多维聚类挖掘的异常检测方法研究
Multidimensional Clustering Based Anomaly Detection Research
摘要
Abstract
Network anomaly detection which is a very important issue in network management has been extensively studied in recent years. Although people in the field made a number of advanced works, the accuracy of automatic classification of network traffic to detect and identify abnormal network traffic is still a very challenging problem. It presents a multidimensional clustering based anomaly detection method,by two stages to achieve anomaly detection. The first phase,through multidimensional clustering algorithms,network traffic is automatically mined into different multidimensional clusters. The second phase calculates the degree of multidimensional clusters to achieve anomaly detection. By this method,the abnormal network traffic is automatically classified into different meaningful clusters,and then these clusters can be used to find network anomalies. Finally,this algorithm was validated through experiments,the results show mat die method can effectively identify abnormal network traffic.关键词
聚类/异常检测/网络安全Key words
clustering/ anomaly detection/ network securit分类
信息技术与安全科学引用本文复制引用
陈平,宋玉蓉,蒋国平..基于多维聚类挖掘的异常检测方法研究[J].计算机技术与发展,2012,22(7):136-139,142,5.基金项目
江苏省自然科学基金项目(BK2010526) （BK2010526）
教育部博士点基金项目(20103223110003) （20103223110003）
南京邮电大学引进人才项目(NY209021) （NY209021）
