A supervised ML Biometric Continuous Authentication System for Industry 4.0

Espín López, Juan Manuel; Esquembre, Francisco; Martínez Pérez, Gregorio; Marín-Blázquez, Javier G.; Huertas Celdrán, Alberto

Publication:
A supervised ML Biometric Continuous Authentication System for Industry 4.0

dc.contributor.author	Espín López, Juan Manuel
dc.contributor.author	Esquembre, Francisco
dc.contributor.author	Martínez Pérez, Gregorio
dc.contributor.author	Marín-Blázquez, Javier G.
dc.contributor.author	Huertas Celdrán, Alberto
dc.contributor.department	Matemáticas
dc.date.accessioned	2024-01-23T11:58:37Z
dc.date.available	2024-01-23T11:58:37Z
dc.date.issued	2022-04-29
dc.description	© 2022. IEEE. This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in IEEE Transactions on Industrial Informatics.	es
dc.description.abstract	Continuous authentication (CA) is a promis- ing approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises unsolved questions regarding machine learning (ML) models: i) its precision and performance, ii) its robustness and iii) the issue about if or when to retrain the models. To answer these questions, this work explores these issues with a proposed supervised vs non-supervised ML-based CA sys- tem that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with Equal Error Rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97%, 62.14%, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled.	es
dc.format	application/pdf	es
dc.format.extent	9	es
dc.identifier.citation	IEEE Transactions on Industrial Informatics
dc.identifier.doi	10.1109/TII.2022.3171321
dc.identifier.uri	http://hdl.handle.net/10201/137591
dc.language	eng	es
dc.publisher	IEEE	es
dc.relation	This research was partially funded by the Spanish Ministry of Science and Innovation, State Research Agency, FEDERfunds, through projects RTI2018-095855-B-I00, MTM2017-84079-P and PID2019-108377RB- C32, by the Swiss Federal Office for Defense Procurement (armasuisse) with the CyberTracer (CYD-C-2020003) project, and by the University of Zu ̈rich UZH.	es
dc.relation.publisherversion	https://ieeexplore.ieee.org/document/9765715	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Continuous Authentication	es
dc.subject	Sensors	es
dc.subject	Applications usage	es
dc.subject	Speaker recognition	es
dc.subject	ML/DL	es
dc.subject	Industry 4.0	es
dc.subject.other	CDU::6 - Ciencias aplicadas	es
dc.title	A supervised ML Biometric Continuous Authentication System for Industry 4.0	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es
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relation.isAuthorOfPublication.latestForDiscovery	155e7fdb-63ef-47a4-8068-bc025cfbabb9