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Journal of Cybersecurity and Information Management

Volume 15 , Issue 1 , PP: 314-331, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Enhancing Anomaly Detection in Industrial Control Systems through Supervised Learning and Explainable Artificial Intelligence

Dhruv G. Bhatt 1 , Parshad U. Kyada 2 , Rajkumar Singh Rathore 3 , M. K. Nallakaruppan 4 * , Faisal Mohammed alotaibi 5 , Rutvij H. Jhaveri 6

  • 1 Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India - (dhruv.bhatt.info@gmail.com)
  • 2 Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India - (parshadkyada2003@gmail.com)
  • 3 Department of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, CF5 2YB Cardiff, U.K - (rsrathore@cardiffmet.ac.uk)
  • 4 Balaji Institute of Modern Management, Sri Balaji University, Pune, Pincode-411033, India - (Nallakaruppan.K@bimmpune.edu.in)
  • 5 Department of Computer Science, Prince Sattam Bin Abdulaziz University, Al-Kharj, Riyadh 16278, Saudi Arabia - (faisal.alotaibi@psau.edu.sa)
  • 6 Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India - (rutvij.jhaveri@sot.pdpu.ac.in)

    • Doi: https://doi.org/10.54216/JCIM.150125

    Received: April 18, 2024 Revised: June 15, 2024 Accepted: August 20, 2024
    Abstract

    This paper addresses industrial control security (ICS) security, focusing on utilizing intrusion detection systems (IDS) to protect ICS networks. It suggests the use of a Measurement Intrusion Detection System (MIDS) over a Network Intrusion Detection System (NIDS), directly analyzing measurement data to detect unseen activities. Training MIDS requires a labeled dataset of various attacks, and a hardware-in-the-loop (HIL) system is used for safer attack simulations. The main aim is to assess MIDS performance through machine learning (ML) on this dataset. Explainable artificial intelligence (XAI) is integrated for transparency in decision-making. Various ML models, such as random forest, achieve high accuracy in detecting anomalies, notably stealthy attacks, with a receiver operating curve (ROC) of 0.9999 and an accuracy of 0.9795. This highlights the importance of machine learning in securing ICS, supported by XAI's explanatory power.

    Keywords :

    Hardware in the Loop (HIL) System , Intrusion Detection , Machine Learning , Real-time Attack Detection , Stealthy Attacks

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    Cite This Article As :
    G., Dhruv. , U., Parshad. , Singh, Rajkumar. , K., M.. , Mohammed, Faisal. , H., Rutvij. Enhancing Anomaly Detection in Industrial Control Systems through Supervised Learning and Explainable Artificial Intelligence. Journal of Cybersecurity and Information Management, vol. , no. , 2025, pp. 314-331. DOI: https://doi.org/10.54216/JCIM.150125
    G., D. U., P. Singh, R. K., M. Mohammed, F. H., R. (2025). Enhancing Anomaly Detection in Industrial Control Systems through Supervised Learning and Explainable Artificial Intelligence. Journal of Cybersecurity and Information Management, (), 314-331. DOI: https://doi.org/10.54216/JCIM.150125
    G., Dhruv. U., Parshad. Singh, Rajkumar. K., M.. Mohammed, Faisal. H., Rutvij. Enhancing Anomaly Detection in Industrial Control Systems through Supervised Learning and Explainable Artificial Intelligence. Journal of Cybersecurity and Information Management , no. (2025): 314-331. DOI: https://doi.org/10.54216/JCIM.150125
    G., D. , U., P. , Singh, R. , K., M. , Mohammed, F. , H., R. (2025) . Enhancing Anomaly Detection in Industrial Control Systems through Supervised Learning and Explainable Artificial Intelligence. Journal of Cybersecurity and Information Management , () , 314-331 . DOI: https://doi.org/10.54216/JCIM.150125
    G. D. , U. P. , Singh R. , K. M. , Mohammed F. , H. R. [2025]. Enhancing Anomaly Detection in Industrial Control Systems through Supervised Learning and Explainable Artificial Intelligence. Journal of Cybersecurity and Information Management. (): 314-331. DOI: https://doi.org/10.54216/JCIM.150125
    G., D. U., P. Singh, R. K., M. Mohammed, F. H., R. "Enhancing Anomaly Detection in Industrial Control Systems through Supervised Learning and Explainable Artificial Intelligence," Journal of Cybersecurity and Information Management, vol. , no. , pp. 314-331, 2025. DOI: https://doi.org/10.54216/JCIM.150125

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