A Blockchain-Based Data-Driven Fault-Tolerant Control System for Smart Factories in Industry 4.0

【Author】 Bin Masood, Abdullah; Hasan, Ammar; Vassiliou, Vasos; Lestas, Marios

【Source】COMPUTER COMMUNICATIONS

【影响因子】5.047

【Abstract】Modern technologies and data-driven approaches have enabled fault-tolerant controllers in Industry 4.0 smart factories to detect, identify, and mitigate anomalies in real-time with a high level of accuracy. However, this has also presented new challenges and requirements for cybersecurity, data analytics, and computational complexity for Data-Driven Fault-Tolerant Controllers (DD-FTC) in smart factories. To address these issues, a Blockchain-Based Data-Driven Fault-Tolerant Control (BB-DD-FTC) framework for smart factories is proposed in this paper. Blockchain ensures the integrity of data logs via its immutable ledger and decentralized architecture. Moreover, the blockchain smart contract functionality, embedded with a Data-Driven Intrusion Detection System (DD-IDS), and reconfiguration conditions, realizes DD-FTC and undertakes the mitigation response in case of cyber-attacks. The DD-IDS mechanism utilizes the principal component analysis technique and observer models, trained via neural networks, to detect an attack and identify the compromised component. The Tennessee Eastman (TE) industrial benchmark process is considered a case study to investigate the performance of the proposed framework. Two kinds of integrity attacks are applied to the sensors of the TE process with simulation results demonstrating the effectiveness of the method in mitigating the adversarial effect of the applied attacks on the overall system performance. As feedback delays can negatively impact performance, a detailed delay analysis is performed using network calculus. The security advantages and limitations of the proposed method are finally highlighted in the performed security analysis. The results are encouraging for the wider adoption of the control-over-the-blockchain concept.

【Keywords】Blockchain; Big data analytics; Fault-tolerant control; Industrial control systems; Smart factory; Industry 4; 0; Tennessee Eastman process

【发表时间】2023 15-Apr

【收录时间】2023-06-05

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-工业领域