Using Random Undersampling and Ensemble Feature Selection for IoT Attack Prediction

Abstract

One consequence of the widespread use of (IoT) devices is an increase in the volume of attacks on (IoT) networks. In this study, we focus on the Bot-IoT dataset, with the aim of classifying its four types of attacks: Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), Reconnaissance, and Information Theft. Our contribution is based on the evaluation of the Random Undersampling (RUS) technique and ensemble Feature Selection Techniques (FSTs). Our results indicate that RUS has a positive impact on overall classification performance. Furthermore, our results show that the FSTs are beneficial for DoS, Reconnaissance, and Information Theft classification but not for DDoS classification. Finally, we note that the ensemble classifiers have generally outperformed the nonensemble classifiers in our study.