Advancing IoMT Defenses: Deep Collaborative Learning for Robust Healthcare Security
Abstract
The Internet of Medical Things (IoMT) plays a pivotal role in healthcare, connecting a myriad of medical devices and applications for efficient patient care. However, the rising prevalence of cyberattacks targeting healthcare institutions for patient data underscores the critical need for robust security measures. This paper introduces a novel Homogenous Collaborative Machine Learning (HCML)-based model to enhance the security of healthcare-connected devices. Utilizing a Deep Neural Network (DNN) algorithm, our model constructs a tailored security framework by integrating multiple device ’edges’, enhancing the system’s ability to thwart cyber threats. We investigate the impact of incorporating additional edges on the model’s performance, employing various metrics and assessing execution times with the IoT healthcare security dataset. Our findings reveal that, compared to conventional centralized learning methods, our proposed HCML model achieves superior generalization, incremental learning, and performance enhancement while maintaining stringent data privacy. This research contributes significantly to the IoMT field by providing a scalable and robust security solution adaptable to the evolving landscape of cyber threats.
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On the IoMT side, stacking ensembles and CNN+LSTM hybrids on CICIoMT2024 reach binary, categorical, and multiclass accuracies near 99%.
“On the IoMT side, stacking ensembles and CNN+LSTM hybrids on CICIoMT2024 reach binary, categorical, and multiclass accuracies near 99% [114,131-133].”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [132]
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Self-citation
- Authors
- Otoum, Y., Singh, P., & Nayak, A.
- Affiliations
- Algoma University (CA)
- University of Ottawa (CA)
- Venue
- GLOBECOM 2024 - 2024 IEEE Global Communications Conference
- Publisher
- IEEE
- Published
- 2024-12-08
- Pages
- 2966-2971
- ISSN
- 1930-529X (ISSN-L)
- OpenAlex
- W4408324949
- Cited by
- 9
- References
- 18
- Citations by year
- 2026: 2 · 2025: 7
- FWCI
- 3.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Globecom (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- Internet of Medical Things, healthcare cybersecurity, collaborative machine learning, deep neural network, edge computing, medical device security, cyberattack detection, data privacy, incremental learning, generalization
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