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

KAMEPALLI UMA; T HARI BABU

doi:10.46243/jst.2022.v7.i08.pp1-8

Authors

KAMEPALLI UMA
T HARI BABU

DOI:

https://doi.org/10.46243/jst.2022.v7.i08.pp1-8

Keywords:

KNN, Clustering, GA, Intrusion Detection

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.

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