Using the K-Nearest Neighbour and Moth Blade Optimization Algorithm to Identify Malicious Sessions in IoT Networks

Abstract

daily lives. Due to the increasing number of potential targets, the security of IoT devices is a pressing issue of the present. In this study, we offer a method for detecting intrusions into IoT networks, which classifies sessions into either attack or regular categories. Work for slection of characteristics for determining the class representative sessions employed a moth flame optimization genetic method. K-Nearest Neighbor was used to determine which class meeting it was. The experimental results, which were obtained using a real dataset, demonstrate that the suggested model, Moth Flame based IOT Network Security (MFIOTNS), is able to optimise different values of the evaluation parameters to provide greater gains in productivity.