Novel Hybrid UNet++ and LSTM Model for Enhanced Attack Detection and Classification in IoMT Traffic
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Date
2025-03-24
Authors
Mezina, Anzhelika
Nurmi, Jari
Ometov, Aleksandr
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Altmetrics
Abstract
The Internet of Medical Things (IoMT) transforms healthcare by allowing real-time monitoring, diagnosis, and treatment using interconnected medical devices and sensors. However, the rapid growth of IoMT brings significant security and privacy challenges due to its critical vulnerability to cyber-attacks. This paper introduces a novel deep learning approach designed to analyze IoMT traffic and identify malicious activities. By leveraging the CIC IoMT dataset, we improved an existing neural network model to improve prediction accuracy. Our approach combines UNet++ and Long Short-Term Memory (LSTM) models to extract network traffic features effectively. Experimental results show that the proposed model outperforms traditional algorithms, achieving an accuracy of 99.92% in anomaly detection and 87.96% in attack categorization. Finally, we highlight the main limitations as well as possibilities for real-world implementation of the approach.
Description
Citation
IEEE Access. 2025, vol. 13, issue April, p. 57589-57603.
https://ieeexplore.ieee.org/document/10937494
https://ieeexplore.ieee.org/document/10937494
Document type
Peer-reviewed
Document version
Published version
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Language of document
en