Evaluation of Tree-Based Machine Learning Algorithms for Network Intrusion Detection in the Internet of Things

Abstract

The Internet of Things (IoT) is receiving increasing attention from academia and industry. However, improving the security of the IoT environment is critical for fostering trust in it and contributing to its growth in the manufacturing market. This study comparatively analyzes current methods for detecting intruders and malicious activities in IoT networks by introducing tree-based machine learning algorithms. It presents a research gap analysis of the current literature. Furthermore, an empirical evaluation study is presented to explore the potential of tree-based approaches to detect intruders in IoT networks. It compares the performance of bagging and boosting techniques in botnet detection by conducting an extensive experimental benchmarking.