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

In: Reliability in Cyber-Physical Systems: The Human Factor Perspective

Author

Listed:
  • Hafida Assmi

    (CRIL—CNRS, Artois University
    Cadi Ayyad University, SISAR Team, LaRTID Laboratory, Technology Higher School Essaouira)

  • Said Jabbour

    (CRIL—CNRS, Artois University)

  • Azidine Guezzaz

    (Cadi Ayyad University, SISAR Team, LaRTID Laboratory, Technology Higher School Essaouira)

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.

Suggested Citation

  • Hafida Assmi & Said Jabbour & Azidine Guezzaz, 2026. "Towards Reliable and Secure IoMT: A Deep Learning Perspective on Cyber-Physical Threats," Springer Series in Reliability Engineering, in: Gururaj H. L. & Vinayakumar Ravi & Hoang Pham & Dayananda P. (ed.), Reliability in Cyber-Physical Systems: The Human Factor Perspective, pages 207-218, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-09917-4_13
    DOI: 10.1007/978-3-032-09917-4_13
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