Abstract: The Internet of Medical Things (IoMT) environment is highly sensitive due to the nature of medical data and its direct connection to patient health, making it a prime target for sophisticated cyberattacks. This study explores the key security challenges within IoMT, discusses how Machine Learning (ML) can enhance threat detection capabilities, and shows how XAI contributes to improving transparency and understanding of model decisions, thereby increasing trust in these systems. It reviews recent advancements in Intrusion Detection Systems (IDS) specifically designed for IoMT networks, with a focus on integrating Explainable Artificial Intelligence (XAI) and ML models. Furthermore, the study compares various algorithms and models, identifying research gaps and discussing different datasets and feature extraction techniques used for optimizing the features. The reported performance and efficiency improvements are derived from prior studies using different dataset sizes, data-splitting strategies, and feature-selection methods.

Aljorey Alqahtani and Monir Abdullah. “A Review on Intrusion Detection Models in Internet of Medical Things (IoMT)”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170134

@article{Alqahtani2026,
title = {A Review on Intrusion Detection Models in Internet of Medical Things (IoMT)},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170134},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170134},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Aljorey Alqahtani and Monir Abdullah}
}