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Related Experiment Videos

Explainable Feature-Group-Aware Cross-Attentive Expert Fusion for IoMT Intrusion Detection.

Asmatullah Khan1, Yang Li1, Ijaz Khan2

  • 1School of Information and Communication Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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This study introduces an explainable intrusion detection system (IDS) for the Internet of Medical Things (IoMT). The framework enhances cybersecurity by providing interpretable threat detection, crucial for protecting sensitive medical data and devices.

Area of Science:

  • Cybersecurity
  • Medical Informatics
  • Artificial Intelligence

Background:

  • The Internet of Medical Things (IoMT) is vital in healthcare but faces significant cybersecurity risks due to increased connectivity.
  • Existing intrusion detection systems (IDS) often lack transparency, operating as black-box models with limited interpretability.

Purpose of the Study:

  • To propose a novel, explainable hybrid IDS framework for multiclass intrusion detection specifically designed for IoMT environments.
  • To enhance the transparency and trustworthiness of IoMT cybersecurity solutions.

Main Methods:

  • Developed a hybrid IDS framework partitioning network traffic features into semantically related groups.
  • Employed specialized expert networks with a gate-balanced Mixture-of-Experts (MoE) routing mechanism and cross-expert self-attention.
Keywords:
FT-TransformerInternet of Medical Things (IoMT)LIMEMixture of ExpertsSHAPTemporal Convolutional Networkcross-attention fusioncyberattack detectionexplainable artificial intelligenceintrusion detection system

Related Experiment Videos

  • Integrated multi-level interpretability using SHAP, LIME, and expert-routing analysis.

    • Main Results:

    • Achieved high accuracy (up to 99.92%) and MCC (up to 0.9988) on CICIoMT2024 and IoMT-TrafficData benchmarks across multiclass settings.
    • Demonstrated effective identification of meaningful traffic features and class-dependent expert specialization.
    • Validated the framework's ability to provide transparent and interpretable intrusion detection decisions.

    Conclusions:

    • The proposed explainable hybrid IDS framework offers an effective and transparent solution for securing IoMT systems.
    • The framework's interpretability enhances trust and understanding of intrusion detection mechanisms in healthcare networks.