%0 Journal Article
%T A Multivariate Online Anomaly Detection Algorithm Based on SVD Updating<br>基于奇异值分解更新的多元在线异常检测方法
%A Qian Ye-kui
%A Chen Ming
%A <br>钱叶魁
%A 陈鸣
%J 电子与信息学报
%D 2010
%I 
%X Network anomaly detection is critical to guarantee stabilized and effective network operation. Although PCA-based network-wide anomaly detection algorithm has good detection performance, it can not satisfy demands of online detection. In order to solve the problem, the traffic matrix model is introduced and a Multivariate Online Anomaly Detection Algorithm based on Singular Value Decomposition Updating named MOADA-SVDU is proposed. The algorithm constructs normal subspace and abnormal subspace incrementally and implements online detection of network traffic anomalies. Theoretic analysis shows that MOADA-SVDU has lower storage and less computing overhead compared with PCA. Analyses for traffic matrix datasets from Internet and simulation experiments show that MOADA-SVDU algorithm not only achieves online detection of network anomaly but also has very good detection performance.
%K Network anomaly detection
%K Online algorithm
%K Singular Value Decomposition (SVD)
%K Multivariate analysis
%K Incremental learning<br>网络异常检测
%K 在线算法
%K 奇异值分解
%K 多元分析
%K 增量学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=8D8D6241AFDDD6E59F7F141AD8B80774&yid=140ECF96957D60B2&vid=9971A5E270697F23&iid=F3090AE9B60B7ED1&sid=B9A14123527DFCC6&eid=78DB415DA3204EAF&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=13