CGAPP: A continuous group authentication privacy-preserving platform for industrial scene

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

Publication:
CGAPP: A continuous group authentication privacy-preserving platform for industrial scene

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	2024-01-24T08:36:50Z
dc.date.available	2024-01-24T08:36:50Z
dc.date.issued	2023-10-09
dc.description	© 2023 The Author(s). Published by Elsevier Ltd. This manuscript version 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 submitted manuscript version of a published work that appeared in final form in Journal of Information Security and Applications	es
dc.description.abstract	In Industry 4.0, security begins with the workers’ authentication, which can be done individually or in groups. Recently, group authentication is gaining momentum, allowing users to authenticate as group members without the need to specify the particular individual. Continuous authentication and federated learning are promising techniques that might help group authentication by providing privacy, by its own design, and extra security compared to traditional methods based on passwords, tokens, or biometrics. However, these techniques have not previously been combined or evaluated for authenticating workers in Industry 4.0. Thus, this paper proposes a novel continuous group authentication privacy-preserving (CGAPP)platform that is suitable for the industry. The CGAPP platform incorporates statistical data from workers’ smartphones and employs federated learning-based outlier detection for group worker authentication while ensuring the privacy of personal data vectors. A series of experiments were performed to measure the framework’s suitability and address the following research questions: (i) What is the cost of using FL compared to full data access in industrial scenarios? (ii) How robust is federated learning against adversarial attacks, specifically, how much malicious data is required to deceive the model? and (iii) How much noise is required to disrupt the authentication system? The results demonstrate the effectiveness of the CGAPP platform in the industry since it provides factory safety while preserving privacy. This platform achieves an accuracy of 92%, comparable to the 96% obtained by traditional approaches in the literature that do not address privacy concerns. The platform’s robustness is tested against attacks in the second and third experiments, and various countermeasures are evaluated. While the CGAPP platform exhibits certain vulnerabilities to data injection attacks, straightforward countermeasures can alleviate them. Nevertheless, the system’s performance experiences a notable impact in the event of a data perturbation attack, and the countermeasures investigated are ineffective in addressing this issue.	es
dc.format	application/pdf	es
dc.format.extent	16	es
dc.identifier.citation	Journal of Information Security and Applications 78 (2023) 103622
dc.identifier.doi	https://doi.org/10.1016/j.jisa.2023.103622
dc.identifier.issn	Electronic: 2214-2126
dc.identifier.uri	http://hdl.handle.net/10201/137633
dc.language	eng	es
dc.publisher	Elsevier ltd.	es
dc.relation	This research was partially funded by the Spanish Ministry of Science and Innovation, State Research Agency, FEDERfunds, Spain, 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, Switzerland (CYD-C-2020003) project, and by the University of Zürich UZH, Switzerland.	es
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S2214212623002065	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	Group authentication	es
dc.subject	Federated learning	es
dc.subject	Adversarial attack	es
dc.subject	Industry 4.0	es
dc.subject.other	CDU::6 - Ciencias aplicadas	es
dc.title	CGAPP: A continuous group authentication privacy-preserving platform for industrial scene	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