135815 20260217205516.0 doi 10.1007/s11227-024-06166-x sideral 138855 ART-2024-138855 eng Ruiz-Villafranca, Sergio A TabPFN-based intrusion detection system for the industrial internet of things 2024 Access copy available to the general public Unrestricted 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. 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 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 2.7 2024 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE 28 / 60 = 0.467 2024 Q2 T2 ENGINEERING, ELECTRICAL & ELECTRONIC 168 / 368 = 0.457 2024 Q2 T2 COMPUTER SCIENCE, THEORY & METHODS 51 / 147 = 0.347 2024 Q2 T2 0.716 2024 Hardware and Architecture 2024 Q2 Theoretical Computer Science 2024 Q2 Software 2024 Q2 Information Systems 2024 Q2 7.1 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Roldán-Gómez, José Castelo Gómez, Juan Manuel Carrillo-Mondéjar, Javier Universidad de Zaragoza Martinez, José Luis 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 80 (2024), 20080–20117 J. supercomput. Journal of Supercomputing 0920-8542 2402170 http://zaguan.unizar.es/record/135815/files/texto_completo.pdf Versión publicada 1335953 http://zaguan.unizar.es/record/135815/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:135815 articulos driver 2026-02-17-20:27:00 ARTICLE