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

Sutharshan Rajasegarar, Alexander Gluhak, Muhammad Ali Imran, Michele Nati, Masud Moshtaghi, Christopher Leckie, Marimutu Palaniswami

Research output: Contribution to journalArticleResearchpeer-review

25 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)2867-2879
Number of pages13
JournalPattern Recognition
Volume47
Issue number9
DOIs
Publication statusPublished - Sep 2014
Externally publishedYes

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