An Adaptive Intrusion Detection System in the Internet of Medical Things Using Fuzzy-Based Learning
Open accessAbstract
The Internet of Medical Things (IoMT) is a growing trend within the rapidly expanding Internet of Things, enhancing healthcare operations and remote patient monitoring. However, these devices are vulnerable to cyber-attacks, posing risks to healthcare operations and patient safety. To detect and counteract attacks on the IoMT, methods such as intrusion detection systems, log monitoring, and threat intelligence are utilized. However, as attackers refine their methods, there is an increasing shift toward using machine learning and deep learning for more accurate and predictive attack detection. In this paper, we propose a fuzzy-based self-tuning Long Short-Term Memory (LSTM) intrusion detection system (IDS) for the IoMT. Our approach dynamically adjusts the number of epochs and utilizes early stopping to prevent overfitting and underfitting. We conducted extensive experiments to evaluate the performance of our proposed model, comparing it with existing IDS models for the IoMT. The results show that our model achieves high accuracy, low false positive rates, and high detection rates, indicating its effectiveness in identifying intrusions. We also discuss the challenges of using static epochs and batch sizes in deep learning models and highlight the importance of dynamic adjustment. The findings of this study contribute to the development of more efficient and accurate IDS models for IoMT scenarios.
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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 [133]
The Results and Discussion section reports IoMT intrusion-detection accuracy of 0.967 for the paper’s FST-LSTM model. The Dataset section identifies WUSTL-EHMS-2020, not CICIoMT2024; the cited paper does not report stacking-ensemble or CNN+LSTM results near 99% across binary, categorical, and multiclass tasks. Its Related Works section mentions a CNN+LSTM approach, but not those claimed results. (LLM, conf=0.88)
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- Authors
- Alalhareth, M., & Hong, S.-C.
- Affiliations
- Towson University (US)
- Department of Computer and Information Sciences, Towson University, Towson, MD 21204, USA
- Corresponding author
- Alalhareth, Mousa
- ORCID
- Alalhareth, Mousa
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-11-17
- Volume
- 23
- Issue
- 22
- Pages
- 9247
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10674369
- OpenAlex
- W4388775013
- Cited by
- 52
- References
- 52
- Citations by year
- 2026: 16 · 2025: 21 · 2024: 15
- FWCI
- 5.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- intrusion detection systems, Internet of Medical Things, early stopping, healthcare cybersecurity, remote patient monitoring, false positive reduction
- MeSH terms
- Machine Learning, Internet of Things, Humans, Intelligence, Neural Networks, Computer, Internet, Memory, Long-Term
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