IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v10y2018i8p76-d162821.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/10/8/76/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/10/8/76/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    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.
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lahza, Hassan & Radke, Kenneth & Foo, Ernest, 2018. "Applying domain-specific knowledge to construct features for detecting distributed denial-of-service attacks on the GOOSE and MMS protocols," International Journal of Critical Infrastructure Protection, Elsevier, vol. 20(C), pages 48-67.
    2. Umer, Muhammad Azmi & Junejo, Khurum Nazir & Jilani, Muhammad Taha & Mathur, Aditya P., 2022. "Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:10:y:2018:i:8:p:76-:d:162821. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.