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Machine Learning Algorithms for Identifying Dependencies in OT Protocols

Author

Listed:
  • Milosz Smolarczyk

    (Research & Development Department, Cryptomage LLC, St. Petersburg, FL 33702, USA)

  • Jakub Pawluk

    (Research & Development Department, Cryptomage SA, 50-556 Wrocław, Poland)

  • Alicja Kotyla

    (Research & Development Department, Cryptomage SA, 50-556 Wrocław, Poland)

  • Sebastian Plamowski

    (Institute of Control and Computation Engineering, Warsaw University of Technology, 00-661 Warsaw, Poland)

  • Katarzyna Kaminska

    (Research & Development Department, Cryptomage SA, 50-556 Wrocław, Poland
    Institute of Telecommunications, Warsaw University of Technology, 00-661 Warsaw, Poland)

  • Krzysztof Szczypiorski

    (Research & Development Department, Cryptomage SA, 50-556 Wrocław, Poland
    Institute of Telecommunications, Warsaw University of Technology, 00-661 Warsaw, Poland)

Abstract

This study illustrates the utility and effectiveness of machine learning algorithms in identifying dependencies in data transmitted in industrial networks. The analysis was performed for two different algorithms. The study was carried out for the XGBoost (Extreme Gradient Boosting) algorithm based on a set of decision tree model classifiers, and the second algorithm tested was the EBM (Explainable Boosting Machines), which belongs to the class of Generalized Additive Models (GAM). Tests were conducted for several test scenarios. Simulated data from static equations were used, as were data from a simulator described by dynamic differential equations, and the final one used data from an actual physical laboratory bench connected via Modbus TCP/IP. Experimental results of both techniques are presented, thus demonstrating the effectiveness of the algorithms. The results show the strength of the algorithms studied, especially against static data. For dynamic data, the results are worse, but still at a level that allows using the researched methods to identify dependencies. The algorithms presented in this paper were used as a passive protection layer of a commercial IDS (Intrusion Detection System).

Suggested Citation

  • Milosz Smolarczyk & Jakub Pawluk & Alicja Kotyla & Sebastian Plamowski & Katarzyna Kaminska & Krzysztof Szczypiorski, 2023. "Machine Learning Algorithms for Identifying Dependencies in OT Protocols," Energies, MDPI, vol. 16(10), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4056-:d:1145709
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    References listed on IDEAS

    as
    1. Milosz Smolarczyk & Sebastian Plamowski & Jakub Pawluk & Krzysztof Szczypiorski, 2022. "Anomaly Detection in Cyclic Communication in OT Protocols," Energies, MDPI, vol. 15(4), pages 1-20, February.
    2. Adrian Jędrzejczyk & Karol Firek & Janusz Rusek, 2022. "Convolutional Neural Network and Support Vector Machine for Prediction of Damage Intensity to Multi-Storey Prefabricated RC Buildings," Energies, MDPI, vol. 15(13), pages 1-16, June.
    3. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    4. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun, 2022. "Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach," Energies, MDPI, vol. 15(13), pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

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