JOURNAL ARTICLE

Enhancing Security in Industrial IoT Through Blockchain-based Authentication Mechanisms

Jun Kong PhiangVivian Yong Siew YeeHafizuddin Bin HilmiDedree Leonna LaiNg Ee ZoeMuhammad Faisal

Year: 2024 Journal:   International Journal of Electrical Engineering Mathematics and Computer Science Vol: 1 (3)Pages: 43-61

Abstract

The Industrial Internet of Things (IIoT) has revolutionized industrial processes, offering automation and data-driven decision-making. However, this interconnectedness brings new security challenges, especially in crucial infrastructure sectors. Traditional security measures are inadequate, leading to the exploration of innovative solutions. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature. This paper proposes a Hybrid Blockchain-Based Authentication Mechanism for IIoT, combining Delegated Proof of Stake (DPoS) and Elliptic Curve Cryptography (ECC). The hybrid architecture utilizes public and private blockchains to ensure scalability, efficiency, and security. Lightweight consensus algorithms, DPoS, are incorporated to optimize performance, while ECC provides efficient cryptographic techniques suitable for IIoT environments. An interoperable framework facilitates seamless integration with existing infrastructure, ensuring regulatory compliance and compatibility. Decentralized identity management further enhances security and privacy. Results and analysis demonstrate the effectiveness of the proposed solution, positioning hybrid blockchain architecture as the most suitable approach for enhancing security in IIoT environments.

Keywords:
Blockchain Computer science Computer security Scalability Industrial Internet Interoperability Authentication (law) Cryptography Internet of Things World Wide Web Database

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Topics

Blockchain Technology Applications and Security
Physical Sciences →  Computer Science →  Information Systems
Physical Unclonable Functions (PUFs) and Hardware Security
Physical Sciences →  Computer Science →  Hardware and Architecture
Brain Tumor Detection and Classification
Life Sciences →  Neuroscience →  Neurology

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