Securing IoT devices using Ensemble Machine Learning in Smart Home Management System

Abstract

In this modern era, we are habituated to small smart home appliances that are typically known as the Internet of Things (IoT) devices. While these devices make our daily life easier with the help of internet connectivity, they are becoming an easy target for attackers to penetrate various cyber-attacks. Distributed Denial of Service (DDoS) is one of the pernicious attacks that can be attempted easily on IoT devices. To make our home smart, these devices play a vital role and as a security expert, it is our responsibility to ensure the security of these IoT devices. In this paper, we propose a security scheme to ensure the IoT devices' security. With the capability of the ensemble machine learning technique, we designed and implemented an intrusion detection system in securing IoT devices. To utilize the full capability of our security solution, we verified it with various well-known and benchmark datasets including the IoT dataset. The results show the efficiency of our security solution by producing better F-1 score and lower false alarms compared to any existing approaches.