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SNN-IoMT: A Novel AI-Driven Model for Intrusion Detection in Internet of Medical Things

Mourad Benmalek1,*,#,*, Abdessamed Seddiki2,#, Kamel-Dine Haouam1

1 Computer Engineering Department, College of Engineering, Al Yamamah University, Riyadh, 11512, Saudi Arabia
2 Ecole Nationale Supérieure d’Informatique, BP 68M, Oued-Smar, Algiers, 16309, Algeria

* Corresponding Author: Mourad Benmalek. Email: email
# These authors contributed equally to this work

(This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)

Computer Modeling in Engineering & Sciences 2025, 143(1), 1157-1184. https://doi.org/10.32604/cmes.2025.062841

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 (Stacked Neural Network Ensemble for IoMT 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.

Keywords

Healthcare; Internet of Medical Things; artificial intelligence; deep learning; intrusion detection system

Cite This Article

APA Style
Benmalek, M., Seddiki, A., Haouam, K. (2025). SNN-IoMT: A Novel AI-Driven Model for Intrusion Detection in Internet of Medical Things. Computer Modeling in Engineering & Sciences, 143(1), 1157–1184. https://doi.org/10.32604/cmes.2025.062841
Vancouver Style
Benmalek M, Seddiki A, Haouam K. SNN-IoMT: A Novel AI-Driven Model for Intrusion Detection in Internet of Medical Things. Comput Model Eng Sci. 2025;143(1):1157–1184. https://doi.org/10.32604/cmes.2025.062841
IEEE Style
M. Benmalek, A. Seddiki, and K. Haouam, “SNN-IoMT: A Novel AI-Driven Model for Intrusion Detection in Internet of Medical Things,” Comput. Model. Eng. Sci., vol. 143, no. 1, pp. 1157–1184, 2025. https://doi.org/10.32604/cmes.2025.062841



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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