Reduced Complexity Intrusion Detection in Sensor Networks Using Genetic Algorithm

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.