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AuthCODE: a privacy-preserving and multi-device continuous authentication architecture based on machine and deep learning

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dc.contributor.authorSánchez Sánchez, Pedro Miguel
dc.contributor.authorFernández Maimó, Lorenzo
dc.contributor.authorMartínez Pérez, Gregorio
dc.contributor.authorHuertas Celdrán, Alberto
dc.contributor.departmentIngeniería y Tecnología de Computadores
dc.date.accessioned2026-01-20T12:56:24Z
dc.date.available2026-01-20T12:56:24Z
dc.date.copyright© 2021 Elsevier Ltd.
dc.date.issued2021-01-04
dc.description.abstractThe authentication field is evolving towards mechanisms able to keep users continuously authenticated without the necessity of remembering or possessing authentication credentials. While relevant limitations of continuous authentication systems -high false positives rates (FPR) and difficulty to detect behaviour changes- have been demonstrated in realistic single-device scenarios, the Internet of Things and next generation of mobile networks (5G) are enabling novel multi-device scenarios, such as Smart Offices, that can help to reduce or address the previous challenges. The paper at hand presents an AI-based, privacy-preserving and multi-device continuous authentication architecture called AuthCODE. AuthCODE seeks to improve single-device solutions limitations by considering additional behavioural data coming from heterogeneous devices. AuthCODE proposes a novel set of features that combine the interactions of users with different devices. The features relevance has been demonstrated in a realistic Smart Office scenario with several users that interact with their mobile devices and personal computers. In this context, a set of single- and multi-device datasets have been generated and published to compare the performance of our multi-device solution against single-device approaches. A pool of experiments with machine and deep learning classifiers measured the impact of time in authentication accuracy and improved the results of single-device approaches by considering multi-device behaviour profiles. Specifically, the multi-device approach using XGBoost with 1-minute window of aggregated features, achieved a 69.33%, 59,65% and 89,35% improvement in the FPR when compared to the single-device approach for computer, mobile applications and mobile sensors respectively. Finally, temporal information classified by a Long-Short Term Memory Network, allowed the identification of additional complex behaviour patterns.
dc.formatapplication/pdf
dc.format.extent14
dc.identifier.citationComputers & Security, 2021, Vol. 103 : 102168
dc.identifier.doihttps://doi.org/10.1016/j.cose.2020.102168
dc.identifier.issn0167-4048
dc.identifier.urihttp://hdl.handle.net/10201/189210
dc.languageeng
dc.publisherElsevier
dc.relationThis work has been partially supported by Armasuisse S+T with project CYD-C-2020003 and Aramis R-3210/047-31, by the University of Zurich UZH, and by the European Union Horizon 2020 Research and Innovation Program under grant agreement no. 830927, namely the H2020 Concordia Project.
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167404820304417
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectMulti device behaviour
dc.subjectSmart office
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectContinuous authentication
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleAuthCODE: a privacy-preserving and multi-device continuous authentication architecture based on machine and deep learning
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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