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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Abstract
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 |
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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. |
Status: | Published |
Refereed: | Yes |
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 |
ISSN: | 2155-5494 |
ISBN: | 9781467319058 |
Published Online: | 23 August 2012 |
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