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ML-FSID-FIS: A Multi-Level Feature Selection and Fuzzy Inference System for Intrusion Detection in IoMT.

Ghaida Balhareth1,2, Mohammad Ilyas1, Basmh Alkanjr1,3

  • 1Department of Electrical Engineering & Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel intrusion detection system for the Internet of Medical Things (IoMT). The ML-FSID-FIS model enhances security by effectively selecting features and using fuzzy logic for accurate threat detection.

Keywords:
Intrusion Detection System (IDS)IoMTfeature selectionfuzzy inference system (FIS)fuzzy logicmachine learning

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Area of Science:

  • Cybersecurity
  • Healthcare Technology
  • Machine Learning

Background:

  • The Internet of Medical Things (IoMT) expands healthcare capabilities but introduces significant security vulnerabilities.
  • Conventional Intrusion Detection Systems (IDSs) face challenges with IoMT traffic, leading to poor accuracy and false alarms.

Purpose of the Study:

  • To develop an advanced Intrusion Detection System (IDS) tailored for IoMT networks.
  • To enhance the security and reliability of IoMT systems through improved intrusion detection.

Main Methods:

  • Proposed ML-FSID-FIS: a multi-level feature selection-based IDS using a fuzzy inference system (FIS).
  • Implemented a three-stage feature selection strategy: Random Forest, XGBoost, ReliefF, Mutual Information, consensus strategy, and ensemble refinement.
  • Selected the top three features for input into the FIS classifier.

Main Results:

  • Achieved strong detection performance on the WUSTL-EHMS-2020 dataset.
  • Maintained a very low false positive rate of 0.3%.
  • Demonstrated superior performance compared to existing methods on the same dataset.

Conclusions:

  • The integrated ML-FSID-FIS approach effectively improves feature efficiency for IoMT intrusion detection.
  • The system offers interpretable intrusion detection, enhancing IoMT network security.