Novel Hybrid UNet++ and LSTM Model for Enhanced Attack Detection and Classification in IoMT Traffic

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197513" target="_blank" >RIV/00216305:26220/26:0197513 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10937494" target="_blank" >https://ieeexplore.ieee.org/document/10937494</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3553966" target="_blank" >10.1109/ACCESS.2025.3553966</a>

Result language

angličtina

Original language name

Novel Hybrid UNet++ and LSTM Model for Enhanced Attack Detection and Classification in IoMT Traffic

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20203 - Telecommunications

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

April

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

57589-57603

UT code for WoS article

001463963000036

EID of the result in the Scopus database

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