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Intrusion Detection System for Wireless Mesh Networks via Improved Whale Optimization

Wireless Mesh networks (WMNs) suffers from abundant security issues because of its dynamic and open communication channels. It is thus risky to formulate an Intrusion Detection System (IDS) that could make out diverse unidentified attacks in the network. This paper intends to propose an Improved Selection of Encircling and Spiral updating position of WO (ISESW) based model for detecting the attacks in WMN systems. The adopted scheme includes two phase’s namely, Feature Selection and Classification. Initially, the features (informative features) from the given data are selected using Principal Component Analysis (PCA) model. The selected informative features are then subjected to classification process using Neural Network (NN), where the presence of attacks is classified. To make the detection more accurate, the weights of NN are fine-tuned using the ISESW algorithm, which is the improved version of WOA model. Finally, the superiority of adopted scheme is evaluated over traditional models in terms of varied measures.