Whale neuro-fuzzy system for intrusion detection in wireless sensor network

Abstract

A hybrid intrusion detection system (IDS) that depends on neuro-fuzzy system (NFS) strategy is proposed which identifies the WSN attacks. IDS which makes utilization of cluster-based engineering with upgraded the low-energy adaptive clustering hierarchy (LEACH) will be simulated for routing that expects to decrease energy utilization level by various sensor nodes. An ID utilizes anomaly detection and misuse detection dependent on NFS which will be changed by incorporating with meta-heuristic optimization strategies for ideally creating fuzzy structure. Fuzzy rule sets alongside the neural network are used to incorporate the location results and determine the attackers kinds of attacks, and the regular procedure of NFS is as per the following: Initially, fuzzy clustering strategy is used to produce distinctive training subsets; in light of unusual training subsets, divergent ANN models are prepared to devise unique base models and fuzzy aggregation module, which is being used to unite these result. The proposed WNFS is created by including the properties of the whale optimization algorithm (WOA) with the neuro-fuzzy architecture. The optimization algorithm selects the appropriate fuzzy rules for the detection.