Enhance intrusion detection in IoT networks using hybrid machine learning techniques

Adedapo Paul Aderemi; Celestine Iwendi; Thaier Hamid; Gbenga Ajatta; Okwuchukwu Innocent Ani

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Enhance intrusion detection in IoT networks using hybrid machine learning techniques

Book chapter

Peer reviewed

Enhance intrusion detection in IoT networks using hybrid machine learning techniques

Adedapo Paul Aderemi, Celestine Iwendi, Thaier Hamid, Gbenga Ajatta and Okwuchukwu Innocent Ani

Proceedings of the 4th International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE’24)

Lecture Notes in Networks and Systems, 1312, Springer

30/11/2025

Abstract

Intrusion Detection System

IDS

Internet of Things (IoT)

Deep Neural Networks (DNN)

Random Forest (RF)

CICIoT2023 dataset

Anomaly Detection

Network Security

Threat Migration

Cybersecurity

Machine Learning

This paper presents a novel Intrusion Detection System (IDS) framework for securing Internet of Things (IoT) networks, leveraging advanced machine learning techniques. The proposed framework integrates Deep Neural Networks (DNNs) and Random Forest (RF) algorithms to enhance detection accuracy and robustness. Utilising the comprehensive CICIoT2023 dataset, the IDS model is rigorously trained and evaluated, demonstrating high efficacy in detecting and mitigating potential threats. However, the results also reveal shortcomings in detecting certain attack categories, such as command injection and SQL injection, indicating areas for further refinement. These findings contribute to the advancement of IoT security through the application of advanced machine learning techniques, while also highlighting the need for continued research to address identified shortcomings.

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Title: Enhance intrusion detection in IoT networks using hybrid machine learning techniques
Creators - without role: Adedapo Paul Aderemi - University of Greater Manchester
Celestine Iwendi - University of Greater Manchester, Computing
Thaier Hamid - University of Greater Manchester, Computing
Gbenga Ajatta - University of Greater Manchester
Okwuchukwu Innocent Ani - Enugu State University of Science and Technology
Publication Details: Proceedings of the 4th International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE’24)
Series: Lecture Notes in Networks and Systems; 1312
Publisher: Springer
Identifiers: 9914009908841
Academic Unit: Computing
Language: English
Resource Type: Book chapter
Date accepted: 09/2024
Comment: Paper accepted for and to be published in the Proceedings of the ICATCE'24 29-30th November, Marrakech, Morocco

Enhance intrusion detection in IoT networks using hybrid machine learning techniques

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