A Comprehensive Analysis of the Machine Learning Algorithms in IoT IDS Systems

Abstract

In this study, we comprehensively compare machine learning algorithms in IoT IDS (Internet of Things Intrusion Detection System) systems from various aspects. We evaluate accuracy, precision, and training time. We examine the effects of data preprocessing techniques including normalization, outlier removal, standardization, and regularization on the datasets. Furthermore, we investigate the impact of dataset balancing, considering both balanced and imbalanced scenarios, on machine learning performance. We also analyzed the contribution of feature selection on the four different datasets. Based on our findings, we observe that certain preprocessing operations provide significant advantages in various ML algorithms, whereas others have very low impact, and their performance varies depending on the dataset and feature selection. The aim of this study is to facilitate the complexity and lengthiness of machine learning processes and algorithm selection, providing insights for future academic research. By addressing this objective, we aim to shed light on simplifying the utilization of machine learning algorithms. The study addresses the challenges arising from the complexity of machine learning processes in IoT IDS systems. This contribution can greatly benefit researchers in their academic endeavors. This multifaceted approach proves beneficial when comparing the methods under consideration, fostering a scientific discourse on their efficacy within contexts.