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A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

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  • Oyeniyi Akeem Alimi

    (Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

  • Khmaies Ouahada

    (Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

  • Adnan M. Abu-Mahfouz

    (Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
    Council for Scientific and Industrial Research, Pretoria 0001, South Africa)

  • Suvendi Rimer

    (Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

  • Kuburat Oyeranti Adefemi Alimi

    (Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

Suggested Citation

  • Oyeniyi Akeem Alimi & Khmaies Ouahada & Adnan M. Abu-Mahfouz & Suvendi Rimer & Kuburat Oyeranti Adefemi Alimi, 2021. "A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9597-:d:622306
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    References listed on IDEAS

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    1. Choubineh, Abouzar & Wood, David A. & Choubineh, Zahak, 2020. "Applying separately cost-sensitive learning and Fisher's discriminant analysis to address the class imbalance problem: A case study involving a virtual gas pipeline SCADA system," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
    2. Al-Daweri, Muataz Salam & Abdullah, Salwani & Ariffin, Khairul Akram Zainol, 2021. "A homogeneous ensemble based dynamic artificial neural network for solving the intrusion detection problem," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).
    3. Abou el Kalam, Anas, 2021. "Securing SCADA and critical industrial systems: From needs to security mechanisms," International Journal of Critical Infrastructure Protection, Elsevier, vol. 32(C).
    4. Ahmed Ahmim & Mohamed Amine Ferrag & Leandros Maglaras & Makhlouf Derdour & Helge Janicke & George Drivas, 2020. "Taxonomy of Supervised Machine Learning for Intrusion Detection Systems," Springer Proceedings in Business and Economics, in: Androniki Kavoura & Efstathios Kefallonitis & Prokopios Theodoridis (ed.), Strategic Innovative Marketing and Tourism, pages 619-628, Springer.
    5. Oyeniyi Akeem Alimi & Khmaies Ouahada & Adnan M. Abu-Mahfouz, 2019. "Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms," Sustainability, MDPI, vol. 11(13), pages 1-18, June.
    6. Krishna Madhuri Paramkusem & Ramazan S. Aygun, 2018. "Classifying Categories of SCADA Attacks in a Big Data Framework," Annals of Data Science, Springer, vol. 5(3), pages 359-386, September.
    7. Yadav, Geeta & Paul, Kolin, 2021. "Architecture and security of SCADA systems: A review," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).
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    Cited by:

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    3. Ali Alzahrani & Theyazn H. H. Aldhyani, 2023. "Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System," Sustainability, MDPI, vol. 15(10), pages 1-29, May.

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