UNSUPERVISED LEARNING BASED DISTRIBUTED DETECTION OF GLOBAL ANOMALIES

Junlin Zhou (), Aleksandar Lazarevic, Kuo-Wei Hsu, Jaideep Srivastava, Yan Fu and Yue Wu
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Junlin Zhou: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
Aleksandar Lazarevic: United Technologies Research Center, East Hartford, Connecticut 06108, USA
Kuo-Wei Hsu: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
Jaideep Srivastava: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
Yan Fu: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
Yue Wu: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China

International Journal of Information Technology & Decision Making (IJITDM), 2010, vol. 09, issue 06, 935-957

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.

Keywords: Distributed anomaly detection; global anomalies; combining models (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1142/S0219622010004172

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