Distributed anomaly detection in wireless sensor networks

Abstract

Identifying misbehaviors is an important challenge for monitoring, fault diagnosis and intrusion detection in wireless sensor networks. A key problem is how to minimise the communication overhead and energy consumption in the network when identifying misbehaviors. Our approach to this problem is based on a distributed, cluster-based anomaly detection algorithm. We minimise the communication overhead by clustering the sensor measurements and merging clusters before sending a description of the clusters to the other nodes. In order to evaluate our distributed scheme, we implemented our algorithm in a simulation based on the sensor data gathered from the Great Duck Island project. We demonstrat..