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All Journal International Journal of Electrical Engineering, Mathematics and Computer Science
Yew Wei Yi
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Computer Science & IT Engineering Mathematics

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Enhancing Aspects of IIoT Networks with Federated Learning Blockchain Integrated Authentication Solution Ling, Fang Ting; Ng Hui Wen; Tsi Shi Ping; Vivian Bong Chiaw Cin; Yew Wei Yi; Muhammad Faisal
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 3 (2024): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i3.17

Abstract

The Industrial Internet of Things (IIoT) faces various challenges in ensuring secure communication, authentication, and data integrity due to its distributed nature and evolving threat landscape. To address these issues, this paper proposes the integration of blockchain authentication as a robust solution to enhance security and reliability in IIoT networks. By leveraging Federated Learning with blockchain technology, the proposed solution aims to improve authentication mechanisms by training models across multiple edge devices, increasing fault tolerance, and adaptability while reducing the risk of single points of failure. The use of blockchain technology ensures a tamper-proof and transparent ledger for securely storing authentication data and model updates, enhancing security and integrity in IIoT networks. The results and analysis demonstrate that the integration of Federated Learning and blockchain technology effectively addresses interoperability issues, performance optimization concerns, and security vulnerabilities within IIoT networks, offering a more efficient, secure, and scalable authentication alternative.
Co-Authors Ling, Fang Ting Muhammad Faisal Ng Hui Wen Tsi Shi Ping Vivian Bong Chiaw Cin