Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Abstract

The rapid proliferation of Internet of Things (IoT) devices has led to an increase in botnet attacks targeting these devices. A botnet attack is a cyber-attack in which a network of compromised devices, referred to as "bots" or "zombies," is utilized to execute a synchronized attack. These attacks can result in substantial harm to both the devices and the network to which they are connected. This study investigates the deployment of security authentication protocols to verify the identity of IoT devices prior to network connection. The study also evaluates the classification accuracy of four distinct supervised machine learning algorithms: Random Forest (RF), Naïve Bayes (NB), DecisionTree (DT), and eXtreme Gradient Boosting (XGBoost). It was foundXGBoost was the best performing classifier among the various machine learning algorithms tested, in terms of detecting botnet attacks in IoT networks using the Bot-IoT dataset.