Intrusion Detection for Wireless Sensor Networks Based on Multi-agent and Refined Clustering

Abstract

In this paper, we put forward a model of multi-agent based on intrusion detection system for wireless sensor networks, and a new method of detection called refined clustering, which is suggested running on some agents. In this new method we use self-organizing map (SOM) neural network to cluster roughly the samples, and the next step the K-means clustering algorithm is adopted to refine the clustering. By the characters of wireless sensor networks and the differences between common nodes and cluster headers, each agent has different tasks and its strategy of detection is also different. These agents carries the new detection method can cooperate with each other, which would make our system have the advantages of high detection rate, good expansibility and lower cost.