A TabPFN-based intrusion detection system for the industrial internet of things

The industrial internet of things (IIoT) has undergone rapid growth in recent years, which has resulted in an increase in the number of threats targeting both IIoT devices and their connecting technologies. However, deploying tools to counter these threats involves tackling inherent limitations, suc...

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Detalles Bibliográficos
Autores: Ruiz-Villafranca, Sergio, Roldán-Gómez, José, Castelo Gómez, Juan Manuel, Carrillo-Mondéjar, Javier, Martinez, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Zaragoza
Repositorio:Zaguán. Repositorio Digital de la Universidad de Zaragoza
OAI Identifier:oai:zaguan.unizar.es:135815
Acceso en línea:http://zaguan.unizar.es/record/135815
Access Level:acceso abierto
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spelling A TabPFN-based intrusion detection system for the industrial internet of thingsRuiz-Villafranca, SergioRoldán-Gómez, JoséCastelo Gómez, Juan ManuelCarrillo-Mondéjar, JavierMartinez, José LuisThe industrial internet of things (IIoT) has undergone rapid growth in recent years, which has resulted in an increase in the number of threats targeting both IIoT devices and their connecting technologies. However, deploying tools to counter these threats involves tackling inherent limitations, such as limited processing power, memory, and network bandwidth. As a result, traditional solutions, such as the ones used for desktop computers or servers, cannot be applied directly in the IIoT, and the development of new technologies is essential to overcome this issue. One approach that has shown potential for this new paradigm is the implementation of intrusion detection system (IDS) that rely on machine learning (ML) techniques. These IDSs can be deployed in the industrial control system or even at the edge layer of the IIoT topology. However, one of their drawbacks is that, depending on the factory’s specifications, it can be quite challenging to locate sufficient traffic data to train these models. In order to address this problem, this study introduces a novel IDS based on the TabPFN model, which can operate on small datasets of IIoT traffic and protocols, as not in general much traffic is generated in this environment. To assess its efficacy, it is compared against other ML algorithms, such as random forest, XGBoost, and LightGBM, by evaluating each method with different training set sizes and varying numbers of classes to classify. Overall, TabPFN produced the most promising outcomes, with a 10–20% differentiation in each metric. The best performance was observed when working with 1000 training set samples, obtaining an F1 score of 81% for 6-class classification and 72% for 10-class classification.2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://zaguan.unizar.es/record/135815reponame:Zaguán. Repositorio Digital de la Universidad de Zaragozainstname:Universidad de ZaragozaInglésinfo:eu-repo/grantAgreement/ES/DGA/T21-23Rinfo:eu-repo/grantAgreement/ES/MICINN/PID2021-123627OB-C52info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131115A-I00info:eu-repo/semantics/openAccessoai:zaguan.unizar.es:1358152026-05-29T13:59:51Z
dc.title.none.fl_str_mv A TabPFN-based intrusion detection system for the industrial internet of things
title A TabPFN-based intrusion detection system for the industrial internet of things
spellingShingle A TabPFN-based intrusion detection system for the industrial internet of things
Ruiz-Villafranca, Sergio
title_short A TabPFN-based intrusion detection system for the industrial internet of things
title_full A TabPFN-based intrusion detection system for the industrial internet of things
title_fullStr A TabPFN-based intrusion detection system for the industrial internet of things
title_full_unstemmed A TabPFN-based intrusion detection system for the industrial internet of things
title_sort A TabPFN-based intrusion detection system for the industrial internet of things
dc.creator.none.fl_str_mv Ruiz-Villafranca, Sergio
Roldán-Gómez, José
Castelo Gómez, Juan Manuel
Carrillo-Mondéjar, Javier
Martinez, José Luis
author Ruiz-Villafranca, Sergio
author_facet Ruiz-Villafranca, Sergio
Roldán-Gómez, José
Castelo Gómez, Juan Manuel
Carrillo-Mondéjar, Javier
Martinez, José Luis
author_role author
author2 Roldán-Gómez, José
Castelo Gómez, Juan Manuel
Carrillo-Mondéjar, Javier
Martinez, José Luis
author2_role author
author
author
author
description The industrial internet of things (IIoT) has undergone rapid growth in recent years, which has resulted in an increase in the number of threats targeting both IIoT devices and their connecting technologies. However, deploying tools to counter these threats involves tackling inherent limitations, such as limited processing power, memory, and network bandwidth. As a result, traditional solutions, such as the ones used for desktop computers or servers, cannot be applied directly in the IIoT, and the development of new technologies is essential to overcome this issue. One approach that has shown potential for this new paradigm is the implementation of intrusion detection system (IDS) that rely on machine learning (ML) techniques. These IDSs can be deployed in the industrial control system or even at the edge layer of the IIoT topology. However, one of their drawbacks is that, depending on the factory’s specifications, it can be quite challenging to locate sufficient traffic data to train these models. In order to address this problem, this study introduces a novel IDS based on the TabPFN model, which can operate on small datasets of IIoT traffic and protocols, as not in general much traffic is generated in this environment. To assess its efficacy, it is compared against other ML algorithms, such as random forest, XGBoost, and LightGBM, by evaluating each method with different training set sizes and varying numbers of classes to classify. Overall, TabPFN produced the most promising outcomes, with a 10–20% differentiation in each metric. The best performance was observed when working with 1000 training set samples, obtaining an F1 score of 81% for 6-class classification and 72% for 10-class classification.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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url http://zaguan.unizar.es/record/135815
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/ES/DGA/T21-23R
info:eu-repo/grantAgreement/ES/MICINN/PID2021-123627OB-C52
info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131115A-I00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv
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dc.source.none.fl_str_mv reponame:Zaguán. Repositorio Digital de la Universidad de Zaragoza
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instname_str Universidad de Zaragoza
reponame_str Zaguán. Repositorio Digital de la Universidad de Zaragoza
collection Zaguán. Repositorio Digital de la Universidad de Zaragoza
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