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Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms

Author

Listed:
  • Ugochukwu Onyekachi Obonna

    (Department of Electrical/Electronic Engineering, Federal University of Technology, Owerri 340110, Nigeria)

  • Felix Kelechi Opara

    (Department of Electrical/Electronic Engineering, Federal University of Technology, Owerri 340110, Nigeria)

  • Christian Chidiebere Mbaocha

    (Department of Electrical/Electronic Engineering, Federal University of Technology, Owerri 340110, Nigeria)

  • Jude-Kennedy Chibuzo Obichere

    (Department of Mechatronics Engineering, Federal University of Technology, Owerri 340110, Nigeria)

  • Isdore Onyema Akwukwaegbu

    (Department of Electrical/Electronic Engineering, Federal University of Technology, Owerri 340110, Nigeria)

  • Miriam Mmesoma Amaefule

    (Department of Mathematics, Federal University of Technology, Owerri 340110, Nigeria)

  • Cosmas Ifeanyi Nwakanma

    (ICT Convergence Research Center, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea)

Abstract

Recently, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks. Examples include the denial-of-service (DoS), distributed denial-of-service (DDoS), and man-in-the-middle (MitM) attacks, and this may have largely been caused by the integration of open network to operation technology (OT) as a result of low-cost network expansion. The connection of OT to the internet for firmware updates, third-party support, or the intervention of vendors has exposed the industry to attacks. The inability to detect these unpredictable cyber-attacks exposes the PCN, and a successful attack can lead to devastating effects. This paper reviews the different forms of cyber-attacks in PCN of oil and gas installations while proposing the use of machine learning algorithms to monitor data exchanges between the sensors, controllers, processes, and the final control elements on the network to detect anomalies in such data exchanges. Python 3.0 Libraries, Deep-Learning Toolkit, MATLAB, and Allen Bradley RSLogic 5000 PLC Emulator software were used in simulating the process control. The outcomes of the experiments show the reliability and functionality of the different machine learning algorithms in detecting these anomalies with significant precise attack detections identified using tree algorithms (bagged or coarse ) for man-in-the-middle (MitM) attacks while taking note of accuracy-computation complexity trade-offs.

Suggested Citation

  • Ugochukwu Onyekachi Obonna & Felix Kelechi Opara & Christian Chidiebere Mbaocha & Jude-Kennedy Chibuzo Obichere & Isdore Onyema Akwukwaegbu & Miriam Mmesoma Amaefule & Cosmas Ifeanyi Nwakanma, 2023. "Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms," Future Internet, MDPI, vol. 15(8), pages 1-19, August.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:280-:d:1221467
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    References listed on IDEAS

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    1. Marcio Andrey Teixeira & Tara Salman & Maede Zolanvari & Raj Jain & Nader Meskin & Mohammed Samaka, 2018. "SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach," Future Internet, MDPI, vol. 10(8), pages 1-15, August.
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