Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3174 2019 2019 Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India M. M. Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India Parshad U. Kyada Department of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, CF5 2YB Cardiff, U.K Rajkumar Singh Rathore Balaji Institute of Modern Management, Sri Balaji University, Pune, Pincode-411033, India M. K. Nallakaruppan Department of Computer Science, Prince Sattam Bin Abdulaziz University, Al-Kharj, Riyadh 16278, Saudi Arabia Faisal Mohammed alotaibi Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India Rutvij H. Jhaveri 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. 2025 2025 314 331 10.54216/JCIM.150125 https://www.americaspg.com/articleinfo/2/show/3174