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Identifying cyber attack vulnerabilities in the main lubricating oil system of marine propulsion units

Author

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  • Gülmez, Yigit
  • Konur, Olgun
  • Erbas, Muhammed
  • Bauk, Sanja

Abstract

This study investigates the cyber vulnerabilities of the main engine lubricating oil system in marine vessels through a detailed simulation model. It focuses on the impact of cyber-attacks targeting temperature control equipment and sensors, using a MATLAB Simulink model to simulate various attack scenarios and assess their effects on system behavior. The research identifies critical components prone to cyber threats and quantifies their risk levels using a novel integration of Monte Carlo simulations, safety margins methodologies and a full mission engine room simulator. The results highlight the impact of each specific cyber-attack on the temperature of the lubricating oil in a marine vessel's main engine, showing how substantially each type of cyber-attack can increase temperatures and potentially lead to catastrophic engine failures. Based on these insights, the study proposes applicable strategies to enhance the cyber resilience of marine vessel systems, highlighting the need for advanced detection mechanisms and comprehensive cyber defense measures. This study not only explores specific vulnerabilities but also provides further research on maritime cybersecurity.

Suggested Citation

  • Gülmez, Yigit & Konur, Olgun & Erbas, Muhammed & Bauk, Sanja, 2025. "Identifying cyber attack vulnerabilities in the main lubricating oil system of marine propulsion units," International Journal of Critical Infrastructure Protection, Elsevier, vol. 51(C).
  • Handle: RePEc:eee:ijocip:v:51:y:2025:i:c:s187454822500071x
    DOI: 10.1016/j.ijcip.2025.100810
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    References listed on IDEAS

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    1. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
    2. 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.
    3. Di Maio, Francesco & Rai, Ajit & Zio, Enrico, 2016. "A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 9-18.
    4. Mathew, Midhya & Kazi, Faruk, 2024. "Hardware-in-Loop (HIL) Testbed Design of Thermal Power Plant for Threat Modeling and Attack Vector Analysis," International Journal of Critical Infrastructure Protection, Elsevier, vol. 45(C).
    5. Di Maio, Francesco & Picoco, Claudia & Zio, Enrico & Rychkov, Valentin, 2017. "Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 122-138.
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