WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance

In recent years we have seen the emergence of new industrial paradigms such as Industry 4.0/5.0 or the Industrial Internet of Things (IIoT). As the use of these new paradigms continues to grow, so do the number of threats and exploits that they face, which makes the IIoT a desirable target for cyber...

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Authors: Ruiz Villafranca, Sergio, Roldán Gómez, José, Carrillo Mondéjar, Javier, Martinez, José Luis, H. Gañán, Carlos
Format: article
Publication Date:2025
Country:España
Institution:Universidad de Castilla-La Mancha
Repository:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/47270
Online Access:https://doi.org/10.1016/j.future.2025.107707
https://hdl.handle.net/10578/47270
Access Level:Open access
Keyword:CyberSecurity
Multi-access edge computing
Machine learning
Intrusion Detection System
Industrial Internet of Things
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spelling WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performanceRuiz Villafranca, SergioRoldán Gómez, JoséCarrillo Mondéjar, JavierMartinez, José LuisH. Gañán, CarlosCyberSecurityMulti-access edge computingMachine learningIntrusion Detection SystemIndustrial Internet of ThingsIn recent years we have seen the emergence of new industrial paradigms such as Industry 4.0/5.0 or the Industrial Internet of Things (IIoT). As the use of these new paradigms continues to grow, so do the number of threats and exploits that they face, which makes the IIoT a desirable target for cybercriminals. Furthermore, IIoT devices possess inherent limitations, primarily due to their limited resources. As a result, it is often impossible to detect attacks using solutions designed for other environments. Recently, Intrusion Detection Systems (IDS) based on Machine Learning (ML) have emerged as a solution that takes advantage of the large amount of data generated by IIoT devices to implement their functionality and achieve good performance, and the inclusion of the Multi-Access Edge Computing (MEC) paradigm in these environments provides the necessary computational resources to deploy IDS effectively. Furthermore, TabPFN has been considered as an attractive option for solving classification problems without the need to reprocess the data. However, TabPFN has certain drawbacks when it comes to the number of training samples and the maximum number of different classes that the model is capable of classifying. This makes TabPFN unsuitable for use when the dataset exceeds one of these limitations. In order to overcome such limitations, this paper presents a Weighted Fusion-Ensemble-based TabPFN (WFE-Tab) model to improve IDS performance in IIoT-MEC scenarios. The presented study employs a novel weighted fusion method to preprocess data into multiple subsets, generating different ensemble family TabPFN models. The resulting WFE-Tab model comprises four stages: data collection, data preprocessing, model training, and model evaluation. The performance of the WFE-Tab method is evaluated using key metrics such as Accuracy, Precision, Recall, and F1-Score, and validated using the Edge-IIoTset public dataset. The performance of the method is then compared with baseline and modern methods to evaluate its effectiveness, achieving an F1-Score performance of 99.81%Elsevier202620262025info:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.1016/j.future.2025.107707https://hdl.handle.net/10578/47270reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglés2022-PRED-206772023-GRIN-34056SBPLY/21/180501/000195PID2021-123627OB-C52MCIN/AEI, Spain/10.13039/501100011033PID2022-142332OA-I00Spain/10.13039/501100011033TED2021-131115A-I00info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/oai:ruidera.uclm.es:10578/472702026-05-27T07:36:41Z
dc.title.none.fl_str_mv WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance
title WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance
spellingShingle WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance
Ruiz Villafranca, Sergio
CyberSecurity
Multi-access edge computing
Machine learning
Intrusion Detection System
Industrial Internet of Things
title_short WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance
title_full WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance
title_fullStr WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance
title_full_unstemmed WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance
title_sort WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance
dc.creator.none.fl_str_mv Ruiz Villafranca, Sergio
Roldán Gómez, José
Carrillo Mondéjar, Javier
Martinez, José Luis
H. Gañán, Carlos
author Ruiz Villafranca, Sergio
author_facet Ruiz Villafranca, Sergio
Roldán Gómez, José
Carrillo Mondéjar, Javier
Martinez, José Luis
H. Gañán, Carlos
author_role author
author2 Roldán Gómez, José
Carrillo Mondéjar, Javier
Martinez, José Luis
H. Gañán, Carlos
author2_role author
author
author
author
dc.subject.none.fl_str_mv CyberSecurity
Multi-access edge computing
Machine learning
Intrusion Detection System
Industrial Internet of Things
topic CyberSecurity
Multi-access edge computing
Machine learning
Intrusion Detection System
Industrial Internet of Things
description In recent years we have seen the emergence of new industrial paradigms such as Industry 4.0/5.0 or the Industrial Internet of Things (IIoT). As the use of these new paradigms continues to grow, so do the number of threats and exploits that they face, which makes the IIoT a desirable target for cybercriminals. Furthermore, IIoT devices possess inherent limitations, primarily due to their limited resources. As a result, it is often impossible to detect attacks using solutions designed for other environments. Recently, Intrusion Detection Systems (IDS) based on Machine Learning (ML) have emerged as a solution that takes advantage of the large amount of data generated by IIoT devices to implement their functionality and achieve good performance, and the inclusion of the Multi-Access Edge Computing (MEC) paradigm in these environments provides the necessary computational resources to deploy IDS effectively. Furthermore, TabPFN has been considered as an attractive option for solving classification problems without the need to reprocess the data. However, TabPFN has certain drawbacks when it comes to the number of training samples and the maximum number of different classes that the model is capable of classifying. This makes TabPFN unsuitable for use when the dataset exceeds one of these limitations. In order to overcome such limitations, this paper presents a Weighted Fusion-Ensemble-based TabPFN (WFE-Tab) model to improve IDS performance in IIoT-MEC scenarios. The presented study employs a novel weighted fusion method to preprocess data into multiple subsets, generating different ensemble family TabPFN models. The resulting WFE-Tab model comprises four stages: data collection, data preprocessing, model training, and model evaluation. The performance of the WFE-Tab method is evaluated using key metrics such as Accuracy, Precision, Recall, and F1-Score, and validated using the Edge-IIoTset public dataset. The performance of the method is then compared with baseline and modern methods to evaluate its effectiveness, achieving an F1-Score performance of 99.81%
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.future.2025.107707
https://hdl.handle.net/10578/47270
url https://doi.org/10.1016/j.future.2025.107707
https://hdl.handle.net/10578/47270
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 2022-PRED-20677
2023-GRIN-34056
SBPLY/21/180501/000195
PID2021-123627OB-C52
MCIN/AEI, Spain/10.13039/501100011033
PID2022-142332OA-I00
Spain/10.13039/501100011033
TED2021-131115A-I00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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