An enhanced Grey Wolf Optimizer based Particle Swarm Optimizer for intrusion detection system in wireless sensor networks

The intrusion detection system is a method for detection against attacks, making it one of the essential defense layers. Researchers are trying to find new algorithms to inspect all inbound and outbound activities and identify suspicious patterns that may show an attempted system attack. The proposed technique for detecting intrusions uses the Grey Wolf Optimization (GWO) to solve feature selection problems and hybridizing it with Particle Swarm Optimization (PSO) to utilize the best value to update the information of each grey wolf position. This technique preserves the individual's best position information by the PSO algorithm, which prevents the GWO algorithm from falling into a local optimum. The NSL KDD dataset is used to verify the performance of the proposed technique. The classification is done using the k-means and SVM algorithms to measure the performance in terms of accuracy, detection rate, false alarm rate, number of features, and execution time. The results have shown that the proposed technique attained the necessary improvement of the GWO algorithm when using K-means or SVM algorithms.