Intrusion Detection System in Wireless Sensor Network using Improved Whale Optimization and Enhanced Fuzzy Neural Network

Wireless sensor networks (WSNs) are regularly employed in risky, uncontrolled situations. WSNs are vulnerable to physical intrusion and security threats. Strong security measures must thus be implemented to secure networks where detecting intrusions are generally acknowledged as one of the most effective security methods for protecting a network from malicious assaults and illegal access. Recent study proposes an improved IDS based on modified binary grey wolf optimizer with support vector machine (GWOSVM-IDS). Optimal wolf counts are found using 3,5,7 wolves. The suggested technique attempts to enhance accuracies of intrusion detections while minimizing processing times with lower false alarm rates and feature counts created by IDS in WSNs. However in existing work sensor nodes consumes more energy to perform packet transmission. More energy consumption may lead to network failure because of this reason energy is the very important parameter in WSNs. Additionally, Grey Wolf Optimizer (GWO) performs poorly in local searches and has a slow convergence rate, both of which might affect intrusion detection effectiveness. Support vector machine (SVM) is unsuitable for managing huge data sets. Increased feature counts per data points during training results in poor SVM performances. To address these challenges, the suggested study suggests node clustering, which is accomplished with weighted KMC. Cluster heads (CHs) will be chosen using Mutation Based Improved Butterfly Optimization (MBIBO). To construct secure communications in WSNs, Improved Whale Optimizations (IWO) for feature selections from input network security laboratory dataset is developed, which reduces time consumption and increases intrusion detection efficiency. Experimental findings demonstrate efficacy of the suggested models in terms of packet delivery ratios, end-to-end latencies, throughputs, and attack detection rates.