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Explainable Deep Learning for Cyber Threat in IoMT: A Synchronization-Enhanced Sparse Autoencoder Approach.

Yang Song, Amel Ksibi, Kadambri Agarwal

IEEE Journal of Biomedical and Health Informatics

|March 9, 2026

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This study introduces an explainable deep learning framework for transparent cyber threat detection in Internet of Medical Things (IoMT) networks, achieving high accuracy and interpretability.

Area of Science:

Cybersecurity
Artificial Intelligence
Medical Informatics

Background:

Interconnected medical devices (Internet of Medical Things - IoMT) face increasing cyber threats.
Traditional deep learning methods for cyber threat intelligence (CTI) lack transparency due to the 'black-box' problem, hindering deployment in critical healthcare settings.
Explainability is crucial for practical application of CTI in IoMT environments.

Purpose of the Study:

To present a novel explainable deep learning (XDL) framework for transparent cyber threat detection in IoMT networks.
To address the limitations of traditional deep learning models in healthcare cybersecurity by providing interpretable decision-making processes.
To develop a robust system for identifying and mitigating cyber threats within IoMT ecosystems.

Main Methods:

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Developed an XDL framework (XDL-CTI-MedNet) integrating sparse autoencoders and neural synchronization mechanisms.
Employed neuron-level local activation consistency constraints and synchronization-based functional module construction for transparent threat detection.
Utilized a multi-dimensional interpretability evaluation system assessing explanation accuracy, stability, purity, and diversity.

Main Results:

Achieved high detection accuracy (98.4-98.8%) and interpretability scores (0.935-0.947) on CIC IoMT 2024 and IoT healthcare security datasets.
Outperformed six baseline methods across all evaluation dimensions, demonstrating superior performance.
Statistical validation confirmed robust performance with low standard deviations (<0.8%) and significant results (p<0.05).

Conclusions:

The proposed XDL-CTI-MedNet framework effectively enhances cyber threat detection in IoMT networks with high accuracy and interpretability.
The framework provides a transparent and reliable solution for critical medical environments where explainable AI is essential.
This research offers a significant advancement in securing IoMT networks against sophisticated cyber threats.