Intrusion detection in sensor networks using clustering and immune systems
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Integrated Computer-Aided Engineering
Anomaly detection in wireless sensor networks: A survey
Journal of Network and Computer Applications
Energy-efficient intrusion detection system for wireless sensor network based on MUSK architecture
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
An Integrated Intrusion Detection System for Cluster-based Wireless Sensor Networks
Expert Systems with Applications: An International Journal
Lightweight energy consumption based intrusion detection system for wireless sensor networks
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Some security protocols or mechanisms have been designed for wireless sensor networks (WSNs). However, an intrusion detection system (IDS) should always be deployed on security critical applications to defense in depth. Due to the resource constraints, the intrusion detection system for traditional network cannot be used directly in WSNs. Several schemes have been proposed to detect intrusions in wireless sensor networks. But most of them aim on some specific attacks (e.g. selective forwarding) or attacks on particular layers, such as media access layer or routing layer. In this paper, we present a framework of machine learning based intrusion detection system for wireless sensor networks. Our system will not be limited on particular attacks, while machine learning algorithm helps to build detection model from training data automatically, which will save human labor from writing signature of attacks or specifying the normal behavior of a sensor node.