IoT Intrusion Detection Using Machine Learning Classifiers and PCA Dimensionality Reduction for N-BaIoT Dataset

Abstract

Recently, the rapid expansion of the Internet of Things (IoT) has opened up new possibilities and introduced significant security challenges. This evolution enhances everyday life but also increases risks in various domestic and industrial contexts due to growing threats such as cyberattacks and intrusions. To protect both domestic activities and industrial infrastructures, it is imperative to address these challenges. This study enhances security in IoT and IIoT by exploring machine learning-based intrusion detection techniques. The primary goal is to strengthen system protection and ensure the continuity of essential operations. Utilizing the N-BaIoT dataset, designed to simulate realistic IoT attack scenarios, we evaluated the effectiveness of various multiclass classification methods, including PCA dimensionality reduction. After extensive data preprocessing and the application of several classifiers such as KNN, Random Forest, Naive Bayes, Decision Tree, Extra Trees, and XGBoost, we built an effective IoT IDS. The Extra Trees algorithm, in combination with PCA, showed the best performance, achieving an impressive 99.94% accuracy. This underscores the effectiveness of machine learning in detecting and mitigating IoT and IIoT cyber threats and highlights the importance of selecting appropriate methods for optimal results in complex security environments.