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

Shahid, Usama, Zunnurain Hussain, Muhammad, Zulkifl Hasan, Muhammad, 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. ISSN 2169-3536

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Abstract

The Internet of Things (IoT) is transforming everyday objects. However, its devices’ limited memory, processing power, and network capabilities 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 utilizes statistical information graphs to investigate network traffic features encompassing all four attacks. 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 the Random Forest classifier achieves 99% accuracy, while Transformers reach 97% F1-Score with a training time of only 16.8 minutes over five epochs.

Item Type:	Article
Uncontrolled Keywords:	black hole attack; data science; decreased rank attack; deep learning; DODAG VNA; flooding attack; Intrusion detection system; IoT; machine learning; routing protocols; RPL; security
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing
Depositing User:	Tamara Malone
Date Deposited:	11 Jun 2025 13:15
Last Modified:	11 Jun 2025 13:15
URI:	https://norma.ncirl.ie/id/eprint/7830

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