📅 15 July 2024
DOI: 10.62951/ijeemcs.v1i3.17
Enhancing Aspects of IIoT Networks with Federated Learning Blockchain Integrated Authentication Solution
International Journal of Electrical Engineering, Mathematics and Computer Science
Asosiasi Riset Teknik Elektro dan Informatika Indonesia (ARTEII)
📄 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.
ℹ️ Informasi Publikasi
Tanggal Publikasi
15 July 2024
Volume / Nomor / Tahun
Volume 1, Nomor 3, Tahun 2024
📝 HOW TO CITE
Ling, Fang Ting; Ng Hui Wen; Tsi Shi Ping; Vivian Bong Chiaw Cin; Yew Wei Yi; Muhammad Faisal, "Enhancing Aspects of IIoT Networks with Federated Learning Blockchain Integrated Authentication Solution," International Journal of Electrical Engineering, Mathematics and Computer Science, vol. 1, no. 3, Jul. 2024.
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