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SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

Author

Listed:
  • Marcio Andrey Teixeira

    (Department of Informatics, Federal Institute of Education, Science, and Technology of Sao Paulo, Catanduva 15808-305, SP, Brazil
    Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, MO 63130, USA)

  • Tara Salman

    (Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, MO 63130, USA)

  • Maede Zolanvari

    (Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, MO 63130, USA)

  • Raj Jain

    (Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, MO 63130, USA)

  • Nader Meskin

    (Department of Electrical Engineering, Qatar University, Doha 2713, Qatar)

  • Mohammed Samaka

    (Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar)

Abstract

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:8:p:76-:d:162821
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    References listed on IDEAS

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    1. Matti Mantere & Mirko Sailio & Sami Noponen, 2013. "Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network," Future Internet, MDPI, vol. 5(4), pages 1-14, September.
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    Cited by:

    1. Sachin Sharma & Saish Urumkar & Gianluca Fontanesi & Byrav Ramamurthy & Avishek Nag, 2022. "Future Wireless Networking Experiments Escaping Simulations," Future Internet, MDPI, vol. 14(4), pages 1-32, April.
    2. Alaa O. Khadidos & Hariprasath Manoharan & Shitharth Selvarajan & Adil O. Khadidos & Khaled H. Alyoubi & Ayman Yafoz, 2022. "A Classy Multifacet Clustering and Fused Optimization Based Classification Methodologies for SCADA Security," Energies, MDPI, vol. 15(10), pages 1-24, May.
    3. 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.
    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. 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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