An adaptive elliptical anomaly detection model for wireless sensor networks

Masud Moshtaghi, Christopher Leckie, Shanika Karunasekera, Sutharshan Rajasegarar

Research output: Contribution to journalArticleResearchpeer-review

25 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)195-207
Number of pages13
JournalComputer Networks
Publication statusPublished - 2014
Externally publishedYes

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