Loading [a11y]/accessibility-menu.js
Online anomaly rate parameter tracking for anomaly detection in wireless sensor networks | IEEE Conference Publication | IEEE Xplore

Online anomaly rate parameter tracking for anomaly detection in wireless sensor networks


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 o...Show More

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.
Date of Conference: 18-21 June 2012
Date Added to IEEE Xplore: 23 August 2012
ISBN Information:

ISSN Information:

Conference Location: Seoul, Korea (South)

References

References is not available for this document.