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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/69122

Title:	UNSUPERVISED LEARNING BASED DISTRIBUTED DETECTION OF GLOBAL ANOMALIES
Authors:	徐國偉 ZHOU,JUNLIN;ALEKSANDAR LAZAREVIC;HSU,KUO-WEI;JAIDEEP SRIVASTAVA;FU,YAN;WU,YUE
Contributors:	資科系
Keywords:	Distributed anomaly detection;global anomalies;combining models
Date:	2010-11
Issue Date:	2014-08-21 14:47:04 (UTC+8)
Abstract:	Anomaly detection has recently become an important problem in many industrial and financial applications. Very often, the databases from which anomalies have to be found are located at multiple local sites and cannot be merged due to privacy reasons or communication overhead. In this paper, a novel general framework for distributed anomaly detection is proposed. The proposed method consists of three steps: (i) building local models for distributed data sources with unsupervised anomaly detection methods and computing quality measure of local models; (ii) transforming local unsupervised local models into sharing models; and (iii) reusing sharing models for new data and combining their results by considering both quality and diversity of them to detect anomalies in a global view. In experiments performed on synthetic and real-life large data set, the proposed distributed anomaly detection method achieved prediction performance comparable or even slightly better than the global anomaly detection algorithm applied on the data set obtained when all distributed data set were merged.
Relation:	International Journal of Information Technology and Decision Making (IJITDM),9(6)935-957
Data Type:	article
Appears in Collections:	[資訊科學系] 期刊論文

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