An adaptive elliptical anomaly detection model for wireless sensor networks

Moshtaghi, Masud, Leckie, Christopher, Karunasekera, Shanika and Rajasegarar, Sutharshan 2014, An adaptive elliptical anomaly detection model for wireless sensor networks, Computer networks, vol. 64, pp. 195-207, doi: 10.1016/j.comnet.2014.02.004.

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Title An adaptive elliptical anomaly detection model for wireless sensor networks
Author(s) Moshtaghi, Masud
Leckie, Christopher
Karunasekera, Shanika
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan
Journal name Computer networks
Volume number 64
Start page 195
End page 207
Total pages 13
Publisher Elsevier
Place of publication Amsterdam. The Netherlands
Publication date 2014-05-08
ISSN 1389-1286
Keyword(s) Adaptive models
Anomaly detection
Clustering hyperellipsoidals
Wireless sensor networks
Elliptical anomaly detection
Summary Wireless Sensor Networks (WSNs) provide a low cost option for monitoring different environments such as farms, forests and water and electricity networks. However, the restricted energy resources of the network impede the collection of raw monitoring data from all the nodes to a single location for analysis. This has stimulated research into efficient anomaly detection techniques to extract information about unusual events such as malicious attacks or faulty sensors at each node. Many previous anomaly detection methods have relied on centralized processing of measurement data, which is highly communication intensive. In this paper, we present an efficient algorithm to detect anomalies in a decentralized manner. In particular, we propose a novel adaptive model for anomaly detection, as well as a robust method for modeling normal behavior. Our evaluation results on both real-life and simulated data sets demonstrate the accuracy of our approach compared to existing methods.
Language eng
DOI 10.1016/j.comnet.2014.02.004
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
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