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Detecting and Handling Cyber-Attacks in Model Predictive Control of Chemical Processes

Author

Listed:
  • Zhe Wu

    (Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA)

  • Fahad Albalawi

    (Department of Electrical and Computer Engineering, Taif University, Taif 21974, Saudi Arabia)

  • Junfeng Zhang

    (Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA)

  • Zhihao Zhang

    (Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA)

  • Helen Durand

    (Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI 48202, USA)

  • Panagiotis D. Christofides

    (Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
    Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095-1592, USA)

Abstract

Since industrial control systems are usually integrated with numerous physical devices, the security of control systems plays an important role in safe operation of industrial chemical processes. However, due to the use of a large number of control actuators and measurement sensors and the increasing use of wireless communication, control systems are becoming increasingly vulnerable to cyber-attacks, which may spread rapidly and may cause severe industrial incidents. To mitigate the impact of cyber-attacks in chemical processes, this work integrates a neural network (NN)-based detection method and a Lyapunov-based model predictive controller for a class of nonlinear systems. A chemical process example is used to illustrate the application of the proposed NN-based detection and LMPC methods to handle cyber-attacks.

Suggested Citation

  • Zhe Wu & Fahad Albalawi & Junfeng Zhang & Zhihao Zhang & Helen Durand & Panagiotis D. Christofides, 2018. "Detecting and Handling Cyber-Attacks in Model Predictive Control of Chemical Processes," Mathematics, MDPI, vol. 6(10), pages 1-22, September.
    • Handle: RePEc:gam:jmathe:v:6:y:2018:i:10:p:173-:d:171832
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    Citations

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    Cited by:

    1. Vijayshankar, Sanjana & Chang, Chin-Yao & Utkarsh, Kumar & Wald, Dylan & Ding, Fei & Balamurugan, Sivasathya Pradha & King, Jennifer & Macwan, Richard, 2023. "Assessing the impact of cybersecurity attacks on energy systems," Applied Energy, Elsevier, vol. 345(C).
    2. Michał Syfert & Andrzej Ordys & Jan Maciej Kościelny & Paweł Wnuk & Jakub Możaryn & Krzysztof Kukiełka, 2022. "Integrated Approach to Diagnostics of Failures and Cyber-Attacks in Industrial Control Systems," Energies, MDPI, vol. 15(17), pages 1-24, August.

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