Augmenting Internet of Medical Things Security: Deep Ensemble Integration and Methodological Fusion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021837" target="_blank" >RIV/62690094:18450/24:50021837 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.techscience.com/CMES/v141n3/58509" target="_blank" >https://www.techscience.com/CMES/v141n3/58509</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmes.2024.056308" target="_blank" >10.32604/cmes.2024.056308</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Augmenting Internet of Medical Things Security: Deep Ensemble Integration and Methodological Fusion

Popis výsledku v původním jazyce

When it comes to smart healthcare business systems, network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults. To protect IoMT devices and networks in healthcare and medical settings, our proposed model serves as a powerful tool for monitoring IoMT networks. This study presents a robust methodology for intrusion detection in Internet of Medical Things (IoMT) environments, integrating data augmentation, feature selection, and ensemble learning to effectively handle IoMT data complexity. Following rigorous preprocessing, including feature extraction, correlation removal, and Recursive Feature Elimination (RFE), selected features are standardized and reshaped for deep learning models. Augmentation using the BAT algorithm enhances dataset variability. Three deep learning models, Transformer-based neural networks, self-attention Deep Convolutional Neural Networks (DCNNs), and Long Short-Term Memory (LSTM) networks, are trained to capture diverse data aspects. Their predictions form a meta-feature set for a subsequent meta-learner, which combines model strengths. Conventional classifiers validate meta-learner features for broad algorithm suitability. This comprehensive method demonstrates high accuracy and robustness in IoMT intrusion detection. Evaluations were conducted using two datasets: the publicly available WUSTL-EHMS-2020 dataset, which contains two distinct categories, and the CICIoMT2024 dataset, encompassing sixteen categories. Experimental results showcase the method’s exceptional performance, achieving optimal scores of 100% on the WUSTL-EHMS-2020 dataset and 99% on the CICIoMT2024. Copyright © 2024 The Authors. Published by Tech Science Press.

Název v anglickém jazyce

Augmenting Internet of Medical Things Security: Deep Ensemble Integration and Methodological Fusion

Popis výsledku anglicky

When it comes to smart healthcare business systems, network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults. To protect IoMT devices and networks in healthcare and medical settings, our proposed model serves as a powerful tool for monitoring IoMT networks. This study presents a robust methodology for intrusion detection in Internet of Medical Things (IoMT) environments, integrating data augmentation, feature selection, and ensemble learning to effectively handle IoMT data complexity. Following rigorous preprocessing, including feature extraction, correlation removal, and Recursive Feature Elimination (RFE), selected features are standardized and reshaped for deep learning models. Augmentation using the BAT algorithm enhances dataset variability. Three deep learning models, Transformer-based neural networks, self-attention Deep Convolutional Neural Networks (DCNNs), and Long Short-Term Memory (LSTM) networks, are trained to capture diverse data aspects. Their predictions form a meta-feature set for a subsequent meta-learner, which combines model strengths. Conventional classifiers validate meta-learner features for broad algorithm suitability. This comprehensive method demonstrates high accuracy and robustness in IoMT intrusion detection. Evaluations were conducted using two datasets: the publicly available WUSTL-EHMS-2020 dataset, which contains two distinct categories, and the CICIoMT2024 dataset, encompassing sixteen categories. Experimental results showcase the method’s exceptional performance, achieving optimal scores of 100% on the WUSTL-EHMS-2020 dataset and 99% on the CICIoMT2024. Copyright © 2024 The Authors. Published by Tech Science Press.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Computer Modeling in Engineering &amp; Sciences

ISSN

1526-1492

e-ISSN

1526-1506

Svazek periodika

141

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

39

Strana od-do

2185-2223

Kód UT WoS článku

001363301400013

EID výsledku v databázi Scopus

2-s2.0-85208240022