Hybrid Intrusion Detection System for RPL IoT Networks Using Machine Learning and Deep Learning

Shahid, Usama ORCID: 0009-0005-6360-333X, Hussain, Muhammad Zunnurain, Hasan, Muhammad Zulkifl, Haider, Ali, Ali, Jibran and Altaf, Jawad (2024) Hybrid Intrusion Detection System for RPL IoT Networks Using Machine Learning and Deep Learning. IEEE Access, 12. pp. 113099-113112. doi:10.1109/ACCESS.2024.3442529

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Abstract

The Internet of Things (IoT) is transforming everyday objects. However, the limited memory, processing power, and network capabilities of its devices make them susceptible to security breaches. The Routing Protocol for Low-Power and Lossy Networks (RPL) is a promising IoT protocol but faces significant security challenges. Existing research often focuses on individual attacks, utilizing various mitigation strategies, including machine learning and deep learning for detection. This paper proposes an Intrusion Detection System (IDS) using the ROUT-4-2023 dataset, which encompasses Black Hole, Flooding, DODAG Version Number, and Decreased Rank attacks. The study investigates network traffic features encompassing all four attacks, utilizing statistical information graphs. Additionally, it experiments with various machine learning models and deep learning architectures for comparative analysis, focusing on confusion matrix outcomes and computational efficiency. Results indicate that Random Forest classifier achieves 99% accuracy, while Transformers reach 97% F1-Score with training time of only 16.8 minutes over 5 epochs.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Security; Internet of Things; Routing protocols; Routing; Prevention and mitigation; Floods; Network topology
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > HD61 Risk in industry. Risk management Q Science > QA Mathematics > QA76 Computer software
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Kamila Niekoraniec
Date Deposited:	05 Sep 2024 16:08
Last Modified:	05 Sep 2024 16:15
URI:	https://eprints.glos.ac.uk/id/eprint/14300

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