Cryptology ePrint Archive: Report 2014/712

Hybrid Anomaly Detection using K-Means Clustering in Wireless Sensor Networks

Mohammad Wazid

Abstract: Security is the biggest concern in Wireless Sensor Networks (WSNs) especially for the ones which are deployed for military applications and monitoring. They are prone to various attacks which degrades the network performance very rapidly. Sometimes multiple attacks are launched in the network using hybrid anomaly. In this situation it is very difficult to find out which kind of anomaly is activated. In this paper, we have proposed a hybrid anomaly detection technique with the application of k-means clustering. The analysis of the network data set consists of traffic data and end to end delay data is performed. The data set is clustered using weka 3.6.10. After clustering, we get the threshold values of various network performance parameters (traffic and delay). These threshold values are used by the hybrid anomaly detection technique to detect the anomaly. During the experimentation, it has been observed that two types of anomalies are activated in the network causing misdirection and blackhole attacks.

Category / Keywords: applications / Hybrid Anomaly, Misdirection, Blackhole, K-Means Clustering, Hybrid Anomaly Detection Algorithm

Date: received 11 Sep 2014

Contact author: wazidkec2005 at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20140912:063455 (All versions of this report)

Short URL: ia.cr/2014/712

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