Intrusion Detection Approach Based on Clustering and Statistical Model for Wireless Sensor Networks

DOI

10.2991/ameii-15.2015.268How to use a DOI?

Keywords

wireless sensor networks; intrusion detection; node clustering; statistical model.

Abstract

In recent years, Intrusion detection has been the focus of security research for Wireless Sensor Networks (WSN). Some approaches or mechanisms have been designed for WSN. But none of them has been widely applied. In this paper, a scheme based on sensor node clustering and statistical model for WSN is presented. First, the sensor nodes are divided into several clusters by using k-means algorithm, and then a kind of anomaly detection algorithm based on statistical model is applied to different clusters for anomaly detection. It is shown through experiments that the scheme can decrease the false alarm rate and increase the detection rate in comparison with existing intrusion detection approaches.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yinghua Zhou
AU  - Hui Shen
PY  - 2015/04
DA  - 2015/04
TI  - Intrusion Detection Approach Based on Clustering and Statistical Model for Wireless Sensor Networks
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 1455
EP  - 1460
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.268
DO  - 10.2991/ameii-15.2015.268
ID  - Zhou2015/04
ER  -