Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Rajasegarar, Sutharshan, Gluhak, Alexander, Ali Imran, Muhammad, Nati, Michele, Moshtaghi, Masud, Leckie, Christopher and Palaniswami, Marimuthu 2014, Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks, Pattern recognition, vol. 47, no. 9, pp. 2867-2879, doi: 10.1016/j.patcog.2014.04.006.

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Title Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks
Author(s) Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736
Gluhak, Alexander
Ali Imran, Muhammad
Nati, Michele
Moshtaghi, Masud
Leckie, Christopher
Palaniswami, Marimuthu
Journal name Pattern recognition
Volume number 47
Issue number 9
Start page 2867
End page 2879
Total pages 13
Publisher Elsevier
Place of publication Chatswood, N.S.W.
Publication date 2014-09-01
ISSN 0031-3203
Keyword(s) Anomaly detection
Outlier factor
Hyperellipsoidal model
Distributed detection
Sensor networks
Summary Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes. © 2014 Elsevier Ltd.
Language eng
DOI 10.1016/j.patcog.2014.04.006
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
Grant ID LP120100529
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089201

Document type: Journal Article
Collection: School of Information Technology
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