State Transition Analysis: A Rule-Based Intrusion Detection Approach
IEEE Transactions on Software Engineering
Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Low-Power Wireless Sensor Networks
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Intrusion Detection in Sensor Networks: A Non-Cooperative Game Approach
NCA '04 Proceedings of the Network Computing and Applications, Third IEEE International Symposium
Decentralized intrusion detection in wireless sensor networks
Proceedings of the 1st ACM international workshop on Quality of service & security in wireless and mobile networks
System approach to intrusion detection using hidden Markov model
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Distributed and control theoretic approach to intrusion detection
IWCMC '07 Proceedings of the 2007 international conference on Wireless communications and mobile computing
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Anomaly detection in wireless sensor networks: A survey
Journal of Network and Computer Applications
Engineering Applications of Artificial Intelligence
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We propose a reduced-complexity genetic algorithm for intrusion detection of resource constrained multi-hop mobile sensor networks. Traditional intrusion detection mechanisms have limited applicability to the sensor networks due to scarce battery and processing resources. Therefore, an effective scheme would require a power efficient and lightweight approach to identify malicious attacks. The goal of this paper is to evaluate sensor node attributes by measuring the perceived threat and its suitability to host local monitoring node (LMN) that acts as trusted proxy agent for the sink and capable of securely monitoring its neighbors. Security attributes in conjunction with genetic algorithm jointly optimizes the placement of monitoring nodes (i.e, LMN) by dynamically evaluating node fitness by profiling workloads patterns, packet statistics, utilization data, battery status, and quality-of-service compliance.