WiFi Intrusion Detection using Artificial Neurons with Bio-inspired Optimization Algorithm
Lenin Narengbam, Shouvik Dey
Abstract
Wireless network growth has been remarkable in recent years. There is a significant increase in the manoeuvring of mobile and stand-alone appliances, with WiFi network connectivity as the main reason for this growth. In electronics products, these devices have become indispensable. Wireless networks have become increasingly vulnerable to attacks as their popularity has grown. Still, high accuracy and detection rate, and low false positive rate are needed for network intrusion detection systems (IDS). In this study, we propose artificial neuron training with a bio-inspired optimization algorithm (BOA) for WiFI networks to efficiently detect intrusion against them. The proposed WiFi intrusion detection framework has artificial neurons trained with Harris Hawks optimization (N-HHO) benchmarked on publicly available Aegean Wi-Fi intrusion Datstaset (AWID). We trained this dataset to propose N-HHO and validate performance evaluation with other BOAs and machine learning (ML) techniques. Our WiFi intrusion detection framework outperforms all other techniques and it could be an alternate option to be used for WiFi network security.