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Securing IoT Networks with Advanced Threat Detection Through Ensemble Methods
Conference proceeding   Peer reviewed

Securing IoT Networks with Advanced Threat Detection Through Ensemble Methods

Adedapo Paul Aderemi, Celestine Iwendi, Thaier Hamid, Okwuchukwu Innocent Ani and Olayinka Anthony Ojo
Proceedings of the 4th International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE’24): Transforming Industries: Harnessing the Power of Artificial Intelligence and the Internet of Things. Volume 1 , pp.24-36
Lecture Notes in Networks and Systems, 1312
ICACTCE: International Conference on Advances in Communication Technology and Computer Engineering (Marrakech, Morocco, 29/11/2024–30/11/2024)
12/07/2025

Abstract

IoT Security Intrusion Detection System Ensemble Learning Machine Learning CICIoT2023 Dataset Cybersecurity
This paper investigates the use of ensemble learning techniques to enhance the security of Internet of Things (IoT) networks through advanced Intrusion Detection Systems (IDS). Utilizing the CICIoT2023 dataset, the study evaluates four ensemble methods: Ensemble Voting, Random Subspace, Bayesian Model Averaging, and Boosting. The results demonstrate that Random Subspace and Bayesian Model Averaging significantly improve detection accuracy and robustness against various attack types, highlighting their potential in real-world IoT security applications.
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