NDLI: Distributed anomaly detection by model sharing

Content Provider	IEEE Xplore Digital Library
Author	Junlin Zhou Deng Jun Yan Fu Yue Wu
Copyright Year	2009
Description	Author affiliation: University of Electronic Science and Technology of China, Chengdu 610054, China (Junlin Zhou; Yan Fu; Yue Wu) \|\| Southwest Jiaotong University, Chengdu 610031, China (Deng Jun)
Abstract	We present a novel general framework for distributed anomaly detection. In the framework, normal behavior is first learned from data from individual data sites using standard anomaly detection algorithms and then these models are combined when predicting anomalies from a new data set. We have investigated seven semi-supervised anomaly detection algorithms for learning normal behavior, as well as proposed method for combining anomaly detection models. Experiments have shown that our proposed combining technique may achieve comparable or even slightly better prediction performance than the anomaly detection models built on the data sets merged from distributed sites.
Starting Page	297
Ending Page	300
File Size	239836
Page Count	4
File Format	PDF
ISBN	9781424452040
DOI	10.1109/ICACIA.2009.5361096
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Intrusion detection Distributed databases Predictive models Covariance matrix Electronic mail Manufacturing Model combining Detection algorithms Distributed computing Monitoring Anomaly detection
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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