IDEAS home Printed from https://ideas.repec.org/a/eee/ijocip/v48y2025ics1874548225000022.html
   My bibliography  Save this article

OptAML: Optimized adversarial machine learning on water treatment and distribution systems

Author

Listed:
  • Ayas, Mustafa Sinasi
  • Kara, Enis
  • Ayas, Selen
  • Sahin, Ali Kivanc

Abstract

This research presents the optimized adversarial machine learning framework, OptAML, which is developed for use in water distribution and treatment systems. In consideration of the physical invariants of these systems, the OptAML generates adversarial samples capable of deceiving a hybrid convolutional neural network-long short-term memory network model. The efficacy of the framework is assessed using the Secure Water Treatment (SWaT) and Water Distribution (WADI) datasets. The findings demonstrate that OptAML is capable of effectively evading rule checkers and significantly reducing the accuracy of anomaly detection frameworks in both systems. Additionally, the study investigates a defense mechanism that demonstrates enhanced robustness against these adversarial attacks and is based on adversarial training. Our results underscore the necessity for robust and flexible protection tactics and highlight the shortcomings of the machine learning-based anomaly detection systems for critical infrastructure that are currently in place.

Suggested Citation

  • Ayas, Mustafa Sinasi & Kara, Enis & Ayas, Selen & Sahin, Ali Kivanc, 2025. "OptAML: Optimized adversarial machine learning on water treatment and distribution systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 48(C).
    • Handle: RePEc:eee:ijocip:v:48:y:2025:i:c:s1874548225000022
    DOI: 10.1016/j.ijcip.2025.100740
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1874548225000022
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijcip.2025.100740?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Majidi, Seyed Hossein & Hadayeghparast, Shahrzad & Karimipour, Hadis, 2022. "FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid," International Journal of Critical Infrastructure Protection, Elsevier, vol. 37(C).
    2. Jia, Yifan & Wang, Jingyi & Poskitt, Christopher M. & Chattopadhyay, Sudipta & Sun, Jun & Chen, Yuqi, 2021. "Adversarial attacks and mitigation for anomaly detectors of cyber-physical systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).
    Full references (including those not matched with items on IDEAS)

    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. Najet Hamdi, 2025. "Enhancing Cybersecurity in smart grid: a review of machine learning approaches," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(2), pages 1-23, June.
    2. Margossian, Harag & Kfouri, Ronald & Saliba, Rita, 2023. "Measurement protection to prevent cyber–physical attacks against power system State Estimation," International Journal of Critical Infrastructure Protection, Elsevier, vol. 43(C).
    3. 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).
    4. Wang, Luping & Wei, Hui & Hao, Yun, 2023. "Vulnerable underground entrance understanding for visual surveillance systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).
    5. Sayed, Mohammad Ali & Ghafouri, Mohsen & Atallah, Ribal & Debbabi, Mourad & Assi, Chadi, 2023. "Protecting the future grid: An electric vehicle robust mitigation scheme against load altering attacks on power grids," Applied Energy, Elsevier, vol. 350(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:eee:ijocip:v:48:y:2025:i:c:s1874548225000022. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-critical-infrastructure-protection .

    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.