Paper 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.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint. MINOR revision.
- Keywords
- Hybrid AnomalyMisdirectionBlackholeK-Means ClusteringHybrid Anomaly Detection Algorithm
- Contact author(s)
- wazidkec2005 @ gmail com
- History
- 2014-09-12: received
- Short URL
- https://ia.cr/2014/712
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2014/712,
author = {Mohammad Wazid},
title = {Hybrid Anomaly Detection using K-Means Clustering in Wireless Sensor Networks},
howpublished = {Cryptology {ePrint} Archive, Paper 2014/712},
year = {2014},
url = {https://eprint.iacr.org/2014/712}
}
