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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

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

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Computer Modeling in Engineering &amp; Sciences

ISSN

1526-1492

e-ISSN

1526-1506

Volume of the periodical

141

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

39

Pages from-to

2185-2223

UT code for WoS article

001363301400013

EID of the result in the Scopus database

2-s2.0-85208240022