The sybil attack in sensor networks: analysis & defenses
Proceedings of the 3rd international symposium on Information processing in sensor networks
Security in wireless sensor networks
Communications of the ACM - Wireless sensor networks
Reputation-based framework for high integrity sensor networks
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
Resilient aggregation in sensor networks
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
Secure time synchronization service for sensor networks
Proceedings of the 4th ACM workshop on Wireless security
Reputation-based framework for high integrity sensor networks
ACM Transactions on Sensor Networks (TOSN)
A Framework of Machine Learning Based Intrusion Detection for Wireless Sensor Networks
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
Linear-Time Computation of Similarity Measures for Sequential Data
The Journal of Machine Learning Research
LIDeA: a distributed lightweight intrusion detection architecture for sensor networks
Proceedings of the 4th international conference on Security and privacy in communication netowrks
Eliminating routing protocol anomalies in wireless sensor networks using AI techniques
Proceedings of the 3rd ACM workshop on Artificial intelligence and security
Bio-inspired enhancement of reputation systems for intelligent environments
Information Sciences: an International Journal
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Security of sensor networks is a complicated task, mostly due to the limited resources of sensor units. Encryption and authentication are useless if an attacker has entered the system. Thus, a second line of defense known as Intrusion Detection must be added in order to detect and eliminate attacks. In the recent past, various solutions for detecting intrusions have been proposed. Most of them are able to detect only a limited number of attacks. The solutions that deploy machine learning techniques exhibit higher level of flexibility and adaptability. Yet, these techniques consume significant power and computational resources. In this work we propose to implement unsupervised algorithms (genetic algorithm and self-organized maps) for detecting intrusions using the energy-efficient SORU architecture. Separate detectors are further organized in a distributed system using the idea of immune system organization. Our solution offers many benefits: ability to detect unknown attacks, high adaptability and energy efficiency. First testing results obtained in real environment demonstrate its high potential.