Rahman, M, Chen, L., Loo, Jonathan ORCID: https://orcid.org/0000-0002-2197-8126 and JIE, WEI ORCID: https://orcid.org/0000-0002-5392-0009 (2023) Towards Deep Learning Based Access Control using Hyperledger-Fabric Blockchain for the Internet of Things. In: 2023 IEEE 6th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), 21-23 Nov 2023, Marrakech, Morocco.

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Abstract

The rapid expansion of Internet of Things (IoT) devices has resulted in significant progress and developments in various sectors, such as smart healthcare, self-driving vehicles, smart banking, smart home, Industry 4.0, etc. Traditional centralised access control methods are inadequate to deploy in decentralised IoT networks. Although the existing Blockchain-based access control approaches provide a better way of managing access permission for IoT systems, they are ineffective in addressing critical security gaps and preventing unauthorised access while detecting malicious anomalies. To address these two challenges in a single platform, in this paper, we propose a novel approach towards a deep-learning-based authorisation solution that is deployed within the Hyperledger-Fabric (HLF) private Blockchain. Our approach allows smart-contract to define attribute-based access control policies augmented with the Artificial Neural Network (ANN) model, which can effectively identify and isolate malicious anomalies and prevent unauthorised access from malicious devices. We run experiments to evaluate our platform in terms of security and performance, and our results show positive indicators essential for addressing security issues in decentralised IoT networks.

Item Type:	Conference or Workshop Item (Paper)
ISBN:	9798350303063
Identifier:	10.1109/CloudTech58737.2023.10366162
Identifier:	10.1109/CloudTech58737.2023.10366162
Subjects:	Computing > Systems > Computer networking
Related URLs:	Publisher
Depositing User:	Marc Forster
Date Deposited:	13 Nov 2024 08:56
Last Modified:	13 Nov 2024 08:56
URI:	https://repository.uwl.ac.uk/id/eprint/12897

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