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Online anomaly rate parameter tracking for anomaly detection in wireless sensor networks
conference contribution
posted on 2012-11-01, 00:00 authored by C O'Reilly, A Gluhak, M Imran, Sutharshan RajasegararSutharshan RajasegararAnomaly 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.
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Sensor, Mesh and Ad Hoc Communications and Networks. IEEE Communications Society Conference (9th : 2012 : Seoul, South Korea)Volume
1Pagination
191 - 199Publisher
IEEELocation
Seoul, South KoreaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2012-06-18End date
2012-06-21ISSN
2155-5486eISSN
2155-5494ISBN-13
9781467319058Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2012, IEEETitle of proceedings
SECON 2012 : Proceedings of the 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and NetworksUsage metrics
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