Online Anomaly Rate Parameter Tracking for Anomaly Detection in Wireless Sensor Networks

O'Reilly, C., Gluhak, A., Imran, M. and Rajasegarar, S. (2012) Online Anomaly Rate Parameter Tracking for Anomaly Detection in Wireless Sensor Networks. In: 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Seoul, Korea, 18-21 Jun 2012, pp. 191-199. ISBN 9781467319058 (doi:10.1109/SECON.2012.6275776)

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Anomaly detection in a Wireless Sensor Network is an important aspect of data analysis in order to facilitate intrusion and event detection. A key challenge is creating optimal classifiers constructed from training sets in which the anomaly rates are varying due to the existence of non-stationary distributions in the data. In this paper we propose an adaptive algorithm that can dynamically adjust the anomaly rate parameter, which can be represented by a model parameter of a one-class quarter-sphere support vector machine. This algorithm operates in an online, iterative manner producing an optimal model for a training set, which is presented sequentially. Our evaluations demonstrate that our algorithm is capable of constructing optimal models for a training set that minimizes the error rate on the classification set compared to a static model, where the anomaly rate is kept stationary.

Item Type:Conference Proceedings
Additional Information:The authors thank the support from REDUCE project grant (no: 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.
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad
Authors: O'Reilly, C., Gluhak, A., Imran, M., and Rajasegarar, S.
College/School:College of Science and Engineering > School of Engineering
Published Online:23 August 2012

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