Open Access

ARTICLE

Blockchain and Smart Contracts with Barzilai-Borwein Intelligence for Industrial Cyber-Physical System

Gowrishankar Jayaraman¹, Ashok Kumar Munnangi², Ramesh Sekaran³, Arunkumar Gopu³, Manikandan Ramachandran^4,*

1 Department of Computer Science and Engineering, JAIN (Deemed-to-be University), Banglore, 562112, Karnataka, India
2 Department of Information Technology, Siddhartha Academy of Higher Education-Deemed to be University, Vijayawada, 520007, India
3 Department of Computer Science and Engineering, Dayananda Sagar University, Bengaluru, 562112, Karnataka, India
4 School of Computing, SASTRA Deemed University, Thanjavur, 613401, Tamil Nadu, India

* Corresponding Author: Manikandan Ramachandran. Email:

Computers, Materials & Continua 2026, 86(3), 36 https://doi.org/10.32604/cmc.2025.071124

Received 31 July 2025; Accepted 24 November 2025; Issue published 12 January 2026

Abstract

Industrial Cyber-Physical Systems (ICPSs) play a vital role in modern industries by providing an intellectual foundation for automated operations. With the increasing integration of information-driven processes, ensuring the security of Industrial Control Production Systems (ICPSs) has become a critical challenge. These systems are highly vulnerable to attacks such as denial-of-service (DoS), eclipse, and Sybil attacks, which can significantly disrupt industrial operations. This work proposes an effective protection strategy using an Artificial Intelligence (AI)-enabled Smart Contract (SC) framework combined with the Heterogeneous Barzilai–Borwein Support Vector (HBBSV) method for industrial-based CPS environments. The approach reduces run time and minimizes the probability of attacks. Initially, secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain (BC) integrated within a BC network. The SC executes the HBBSV strategy to verify the security consensus. The Barzilai–Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions. When a component remains within these conditions, no security breach occurs. Conversely, if a component does not satisfy the condition boundaries, a security lapse is detected, and those components are isolated. The HBBSV method thus strengthens protection against DoS, eclipse, and Sybil attacks. Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.

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Keywords

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