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

Rajasegarar, S., Gluhak, A., Imran, M. A. , Nati, M., Moshtaghi, M., Leckie, C. and Palaniswami, M. (2014) Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks. Pattern Recognition Letters, 47, pp. 2867-2879. (doi: 10.1016/j.patcog.2014.04.006)

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.

• Item Type: Articles
• Additional Information: The authors thank the support from REDUCE project Grant (EP/I000232/1) under the Digital Economy Programme run by Research Councils UK – a cross council initiative led by EPSRC and contributed to by AHRC, ESRC and MRC; the Australian Research Council (ARC) Research Network on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) and the ARC Grants (LP120100529 and LE120100129).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad
• Authors: Rajasegarar, S., Gluhak, A., Imran, M. A., Nati, M., Moshtaghi, M., Leckie, C., and Palaniswami, M.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: Pattern Recognition Letters
• Publisher: Elsevier
• ISSN: 0167-8655
• ISSN (Online): 1872-7344
• Published Online: 12 April 2014