Machine Learning for Multi-Classification of Botnets Attacks

Abstract

The number of Internet of Things (IoT) devices connected to the network enlarges dramatically these days. This leads to rising cyberattacks, such as botnets. These attacks result in disrupting the transition of networks and services for IoT devices. Recently, various approaches of Machine Learning (ML) and Deep Learning (DL) have been proposed to detect botnet attacks in the IoT environment. However, the ML/DL methods used in the research are just binary classification between normal and attack classes. In this study, we propose an approach using ML algorithms to develop multi-classification botnet detection systems for IoT devices. The proposed approach is based on not only multi-classification metrics but also the time complexity of the training and testing processes. The multi-classification metrics, particularly multi- classification confusion matrix, Accuracy, Macro F1-score, Micro F1-score, Weighted F1-score, Mathews Correlation Coefficient (MCC), and Cohen Kappa score are used in the entire study. Through the extensive experiments with the N-BaIoT dataset, the Artificial Neural Network classifier has proven its robust performance in terms of both multi-classification metrics and time complexity.