Citation

Bar, Shachar and Prasad, P. W. C. and Sayeed, Md. Shohel (2024) Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence. Computers, Materials & Continua, 81 (1). pp. 1-23. ISSN 1546-2226

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Abstract

Escalating cyber security threats and the increased use of Internet of Things (IoT) devices require utilisation of the latest technologies available to supply adequate protection. The aim of Intrusion Detection Systems (IDS) is to prevent malicious attacks that corrupt operations and interrupt data flow, which might have significant impact on critical industries and infrastructure. This research examines existing IDS, based on Artificial Intelligence (AI) for IoT devices, methods, and techniques. The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy, precision, recall and F1-score; this research also considers training time. Results demonstrate that Graph Neural Networks (GNN) have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99% accuracy in a relatively short training time, while also capable of learning from network traffic the inherent characteristics of different cyber-attacks. These findings identify the GNN (a Deep Learning AI method) as the most efficient IDS system. The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection. This research recommends Federated Learning (FL) as the AI training model, which increases data privacy protection and reduces network data flow, resulting in a more secure and efficient IDS solution.

Item Type:	Article
Uncontrolled Keywords:	Anomaly detection, artificial intelligence, cyber security, data privacy
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Nov 2024 01:32
Last Modified:	04 Nov 2024 01:32
URII:	http://shdl.mmu.edu.my/id/eprint/13103

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