@Article{cmes.2025.062841,
AUTHOR = {Mourad Benmalek, Abdessamed Seddiki, Kamel-Dine Haouam},
TITLE = {SNN-IoMT: A Novel AI-Driven Model for Intrusion Detection in Internet of Medical Things},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {143},
YEAR = {2025},
NUMBER = {1},
PAGES = {1157--1184},
URL = {http://www.techscience.com/CMES/v143n1/60477},
ISSN = {1526-1506},
ABSTRACT = {The Internet of Medical Things (IoMT) connects healthcare devices and sensors to the Internet, driving transformative advancements in healthcare delivery. However, expanding IoMT infrastructures face growing security threats, necessitating robust Intrusion Detection Systems (IDS). Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems, especially when securing interconnected medical devices. This paper introduces SNN-IoMT (<b>S</b>tacked <b>N</b>eural <b>N</b>etwork Ensemble for <b>IoMT</b> Security), an AI-driven IDS framework designed to secure dynamic IoMT environments. Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), the model optimizes data management and integration while ensuring system scalability and interoperability. Trained on the WUSTL-EHMS-2020 and IoT-Healthcare-Security datasets, SNN-IoMT surpasses existing IDS frameworks in accuracy, precision, and detecting novel threats. By addressing the primary challenges in AI-driven healthcare systems, including privacy, reliability, and ethical data management, our approach exemplifies the importance of AI to enhance security and trust in IoMT-enabled healthcare.},
DOI = {10.32604/cmes.2025.062841}
}