Identifying misbehaviors is an important task for intrusion detection in wireless sensor networks. The communication overhead and energy consumption should reach the minimum value to the greatest extent in the network when identifying misbehaviors. Our approach to this problem is based on a distributed

cluster-based anomaly detection algorithm

namely 3N algorithm. We minimize the communication overhead by clustering the sensor measurements and merging clusters before sending a description of the clusters to the other nodes.Resultsshow that distributed scheme achieves comparable accuracy compared to a centralized scheme with a significant reduction in communication overhead.