Towards Reliable and Secure IoMT: A Deep Learning Perspective on Cyber-Physical Threats

Hafida Assmi, Said Jabbour () and Azidine Guezzaz ()
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Hafida Assmi: CRIL—CNRS, Artois University
Said Jabbour: CRIL—CNRS, Artois University
Azidine Guezzaz: Cadi Ayyad University, SISAR Team, LaRTID Laboratory, Technology Higher School Essaouira

A chapter in Reliability in Cyber-Physical Systems: The Human Factor Perspective, 2026, pp 207-218 from Springer

Abstract: Abstract Intrusion detection within Internet of Medical Things (IoMT) environments is complicated by the diversity of communication protocols and the continuous emergence of sophisticated security threats. This research presents a hybrid deep learning framework that harnesses the complementary capabilities of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance intrusion detection in healthcare IoMT environments. Our CNN-LSTM architecture employs CNN for extracting spatial features and LSTM for modeling temporal dependencies, specifically designed for IoMT data. Evaluated using the CICIoMT2024 dataset, covering Bluetooth, WiFi, and MQTT protocols with 18 attack types grouped into five classes, the model achieved an accuracy of 86.24% in multi-class classification, along with strong precision (0.865), recall (0.863), and F1-score (0.863) metrics.

Keywords: Healthcare security; IoMT; Intrusion detection; Deep learning; CNN; LSTM (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-09917-4_13

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