Publication: A supervised ML Biometric Continuous Authentication System for Industry 4.0
Authors
Espín López, Juan Manuel ; Esquembre, Francisco ; Martínez Pérez, Gregorio ; Marín-Blázquez, Javier G. ; Huertas Celdrán, Alberto
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Publisher
IEEE
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DOI
10.1109/TII.2022.3171321
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info:eu-repo/semantics/article
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.
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.
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Citation
IEEE Transactions on Industrial Informatics
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