Distributed Anomaly Detection in Wireless Sensor Networks
- 1 January 2006
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Identifying misbehaviors is an important challenge for monitoring, fault diagnosis and intrusion detection in wireless sensor networks. A key problem is how to minimize 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 minimize 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 demonstrate that our scheme achieves comparable accuracy compared to a centralized scheme with a significant reduction in communication overheadKeywords
This publication has 10 references indexed in Scilit:
- Intrusion Detection for Routing Attacks in Sensor NetworksInternational Journal of Distributed Sensor Networks, 2006
- Clustering distributed data streams in peer-to-peer environmentsInformation Sciences, 2006
- Learning Rules and Clusters for Anomaly Detection in Network TrafficPublished by Springer Science and Business Media LLC ,2005
- Decentralized intrusion detection in wireless sensor networksPublished by Association for Computing Machinery (ACM) ,2005
- An analysis of a large scale habitat monitoring applicationPublished by Association for Computing Machinery (ACM) ,2004
- Security in wireless sensor networksCommunications of the ACM, 2004
- Energy-aware wireless microsensor networksIEEE Signal Processing Magazine, 2002
- A Geometric Framework for Unsupervised Anomaly DetectionPublished by Springer Science and Business Media LLC ,2002
- Efficient algorithms for mining outliers from large data setsPublished by Association for Computing Machinery (ACM) ,2000
- BIRCH: A New Data Clustering Algorithm and Its ApplicationsData Mining and Knowledge Discovery, 1997