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Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems

Author

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  • Mazen Gazzan

    (Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA
    College of Computer Science and Information Systems, Najran University, Najran P.O. Box 1988, Saudi Arabia)

  • Frederick T. Sheldon

    (Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA)

Abstract

Industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems, which control critical infrastructure such as power plants and water treatment facilities, have unique characteristics that make them vulnerable to ransomware attacks. These systems are often outdated and run on proprietary software, making them difficult to protect with traditional cybersecurity measures. The limited visibility into these systems and the lack of effective threat intelligence pose significant challenges to the early detection and prediction of ransomware attacks. Ransomware attacks on ICS and SCADA systems have become a growing concern in recent years. These attacks can cause significant disruptions to critical infrastructure and result in significant financial losses. Despite the increasing threat, the prediction of ransomware attacks on ICS remains a significant challenge for the cybersecurity community. This is due to the unique characteristics of these systems, including the use of proprietary software and limited visibility into their operations. In this review paper, we will examine the challenges associated with predicting ransomware attacks on industrial systems and the existing approaches for mitigating these risks. We will also discuss the need for a multi-disciplinary approach that involves a close collaboration between the cybersecurity and ICS communities. We aim to provide a comprehensive overview of the current state of ransomware prediction on industrial systems and to identify opportunities for future research and development in this area.

Suggested Citation

  • Mazen Gazzan & Frederick T. Sheldon, 2023. "Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems," Future Internet, MDPI, vol. 15(4), pages 1-18, April.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:4:p:144-:d:1118514
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    References listed on IDEAS

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    1. Nai Fovino, Igor & Carcano, Andrea & Masera, Marcelo & Trombetta, Alberto, 2009. "An experimental investigation of malware attacks on SCADA systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 2(4), pages 139-145.
    2. Yahye Abukar Ahmed & Shamsul Huda & Bander Ali Saleh Al-rimy & Nouf Alharbi & Faisal Saeed & Fuad A. Ghaleb & Ismail Mohamed Ali, 2022. "A Weighted Minimum Redundancy Maximum Relevance Technique for Ransomware Early Detection in Industrial IoT," Sustainability, MDPI, vol. 14(3), pages 1-15, January.
    3. Qasem Abu Al-Haija & Abdallah A. Smadi & Mohammed F. Allehyani, 2021. "Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management," Energies, MDPI, vol. 14(21), pages 1-19, October.
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    Cited by:

    1. Mazen Gazzan & Frederick T. Sheldon, 2023. "An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction," Future Internet, MDPI, vol. 15(10), pages 1-18, September.

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