doi:

DOI: 10.3724/SP.J.1219.2013.00670

Information and Control (信息与控制) 2013/42:6 PP.670-676

A Hierarchical Intrusion Detection Model in Wireless Sensor Networks


Abstract:
A two-level intrusion detection model of wireless sensor networks (WSNs) is proposed to detect the attacks in WSNs. The principal component analysis is adopted to reduce the feature dimension and the complexity of data storage and computation. In the cluster level, for the security of sensors, normal sensor nodes employ the transductive confidence machines for K-nearest neighbors for anomaly detection, and cluster heads use the support vector machine based on particle swarm parameters optimization to classify the misuse detections for the detected anomaly data. In the base station level, for the security of cluster heads, the anomaly detection and misuse detection technologies are combined to deal with the monitoring data delivered by cluster heads, which improves the detection probability while preserving low false alarm probability. Simulation results show that the proposed detection algorithm can improve the accuracy of detection even in the case of small samples.

Key words:wireless sensor network,hierarchical intrusion detection,transductive confidence machine for K-nearest neighbors,particle swarm optimization,support vector machine

ReleaseDate:2015-04-15 18:52:37



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