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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

April

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

57589-57603

Kód UT WoS článku

001463963000036

EID výsledku v databázi Scopus

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