Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble Models

作     者：Sama, Najm Us Ullah, Saeed Kazmi, S. M. Ahsan Mazzara, Manuel 

作者机构：Univ Derby Sch Comp Derby DE22 3AW England Univ West England Sch Comp Sci & Creat Technol Bristol BS16 1QY England Innopolis Univ Inst Software Dev & Engn Innopolis 420500 Russia 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：8375-8392页

核心收录：

基　　金：University of Derby 

主　　题：Internet of Things Accuracy Intrusion detection Machine learning Feature extraction Security Nearest neighbor methods Support vector machines Organizations Classification algorithms Internet of Things (IoT) intrusion detection systems (IDS) machine learning classifiers 

摘      要：As the Internet of Things (IoT) landscape rapidly evolves, robust network security measures are imperative. In particular, Intrusion Detection Systems play a very important role in the preservation of an IoT environment from malicious activities. This paper provides a comprehensive performance comparison of various machine learning classifiers, including K-Nearest Neighbors, Gradient Boosting, XGBoost, Support Vector Machines, Random Forests, Decision Trees, and Extremely Randomized Trees, for intrusion detection in IoT networks. Comparative analysis shows that although all models did very well, the ensemble methods-GB, XGBoost, RF, and ERT-constantly performed better than others in F1-Score, recall, accuracy, and precision. Among them, ERT is turned out to be the most effective model for real-time attack detection on IoT devices, with an accuracy of 99.7% besides excellent precision and recall. XGBoost and RF also turn out to have high reliability and accuracy with F1-Scores of 0.95. These findings further underscore that ensemble methods outperform in intrusion detection for IoT networks and, thus, offer important insights to improve security within networks and protect critical IoT-based infrastructures from a variety of threats.