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Related Experiment Video

Updated: Jun 5, 2025

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Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection.

Archana Manoharan1, Manigandan Thathan2

  • 1Department of Electronics and Communication, Dr. N.G.P Institute of Technology, Coimbatore, India. archanaece23@gmail.com.

Scientific Reports
|December 5, 2024
PubMed
Summary
This summary is machine-generated.

A new intrusion detection model, Group Teaching Optimized Probabilistic Deep Auto-Encoder (GTPDA), enhances Internet of Medical Things (IoMT) security. It achieves high accuracy, precision, and recall, outperforming traditional methods for IoMT network protection.

Keywords:
Cyber-attacksDeep learningInformation securityInternet of medical thingsIntrusion detection system

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Area of Science:

  • Cybersecurity
  • Network Security
  • Internet of Medical Things (IoMT)

Background:

  • Internet of Medical Things (IoMT) security is a critical global challenge.
  • Traditional security methods for IoMT suffer from high false positives and low detection rates.
  • Effective security is vital for the successful implementation of IoMT.

Purpose of the Study:

  • To develop a novel intrusion detection model for enhanced IoMT network security.
  • To address the limitations of existing security methodologies in IoMT environments.
  • To improve the accuracy and efficiency of intrusion detection in IoMT.

Main Methods:

  • Data transformation and normalization were applied to balance dataset properties.
  • An Intriguing Group Teaching Optimization (IGTO) algorithm selected essential features for intrusion detection.
  • A Conditional Probabilistic Deep Auto-Encoder (CPDAE) model was employed for accurate intrusion classification.

Main Results:

  • The proposed Group Teaching Optimized Probabilistic Deep Auto-Encoder (GTPDA) model demonstrated significant performance.
  • GTPDA achieved 98.8% precision, 99% recall, 98.8% F1-score, and 99% accuracy.
  • Performance was evaluated using BoT-IoT, Kaggle invasion, and ToN-IoT datasets.

Conclusions:

  • The GTPDA model offers a groundbreaking solution for IoMT security.
  • The proposed model effectively enhances the security of IoMT networks.
  • GTPDA shows superior performance compared to existing methods for detecting intrusions in IoMT.