ArticleSensors (Basel, Switzerland)2024
Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- MSCA-Net: A Multi-Scale Depthwise Attention Network for Multi-Class Intrusion Detection in Internet of Medical Things.Sensors (Basel, Switzerland) · 2026Article
- Hybrid RF FSO communication for 6G improving secrecy and performance under composite weibull lognormal fading and turbulence.Scientific reports · 2026Article
- AI-Driven Hybrid Detection and Classification Framework for Secure Sleep Health IoT Networks.Clocks & sleep · 2026Review
- ML-FSID-FIS: A Multi-Level Feature Selection and Fuzzy Inference System for Intrusion Detection in IoMT.Sensors (Basel, Switzerland) · 2026Article
- A novel adaptive hybrid intrusion detection system with lightweight optimization for enhanced security in internet of medical things.Scientific reports · 2025Article
- A Novel Internet of Medical Things Hybrid Model for Cybersecurity Anomaly Detection.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Survey on Intrusion Detection Systems for Healthcare 5.0: Concepts, Challenges, and Practical Applications.Sensors (Basel, Switzerland) · 2025Review
- A Hybrid Approach for IoT Security: Combining Ensemble Learning with Fuzzy Logic.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2025Review
- Online Meta-Recommendation of CUSUM Hyperparameters for Enhanced Drift Detection.Sensors (Basel, Switzerland) · 2025Article
- A deep Reinforcement learning-based robust Intrusion Detection System for securing IoMT Healthcare Networks.Frontiers in medicine · 2025Article
- RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system.Frontiers in digital health · 2024Article
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Authors and funding
2 authors.
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Abstract
This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model's superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.
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