ArticleSensors (Basel, Switzerland)2025
Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Trustworthy AI-Powered Intrusion Detection for the Internet of Medical Things (IoMT): A Review.Sensors (Basel, Switzerland) · 2026Review
- Trustworthy and ethical intrusion detection for healthcare internet of medical things using reinforcement learning and governance rules.Scientific reports · 2026Article
- Enhancing intrusion detection in encrypted DoH traffic through a robust ensemble learning framework.PloS one · 2026Article
- An explainable multi-head attention network for healthcare IoT threat detection based on the MedDefender-MHAN framework.PloS one · 2026Article
- Recurrent Neural Networks with Integrated Gradients Explanation for Predicting the Hysteresis Behavior of Shape Memory Alloys.Sensors (Basel, Switzerland) · 2025Article
- A novel adaptive hybrid intrusion detection system with lightweight optimization for enhanced security in internet of medical things.Scientific reports · 2025Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Integration of metaheuristic based feature selection with ensemble representation learning models for privacy aware cyberattack detection in IoT environments.Scientific reports · 2025Article
- Real-Time American Sign Language Interpretation Using Deep Learning and Keypoint Tracking.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This paper presents a novel intrusion detection system (IDS) specifically designed for IoMT networks. The proposed IDS leverages machine learning (ML) and deep learning (DL) techniques, employing a stacking ensemble method to enhance detection accuracy by integrating the strengths of multiple classifiers. To ensure real-time performance, the IDS is implemented within a Kappa Architecture framework, enabling continuous processing of IoMT data streams. The system effectively detects and classifies a wide range of cyberattacks, including ARP spoofing, DoS, Smurf, and Port Scan, achieving an outstanding detection accuracy of 0.991 in binary classification and 0.993 in multi-class classification. This research highlights the potential of combining advanced ML and DL methods with ensemble learning to address the unique cybersecurity challenges of IoMT systems, providing a reliable and scalable solution for safeguarding healthcare services.
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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.