ArticleSensors (Basel, Switzerland)2024
Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Trustworthy AI-Powered Intrusion Detection for the Internet of Medical Things (IoMT): A Review.Sensors (Basel, Switzerland) · 2026Review
- AI-Driven Hybrid Detection and Classification Framework for Secure Sleep Health IoT Networks.Clocks & sleep · 2026Review
- Spatial-temporal graph neural network with autoencoder pretraining for intrusion detection in healthcare IoT ecosystems.Scientific reports · 2026Article
- FedSMOTE-DP: Privacy-Aware Federated Ensemble Learning for Intrusion Detection in IoMT Networks.Sensors (Basel, Switzerland) · 2026Article
- False data injection attack dataset for classification, identification, and detection for IIoT in Industry 5.0.Data in brief · 2025Article
- Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.