Navigation auf zora.uzh.ch

Search ZORA

ZORA (Zurich Open Repository and Archive)

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

Sánchez Sánchez, Pedro Miguel; Fernández Maimó, Lorenzo; Huertas Celdrán, Alberto; Martínez Pérez, Gregorio (2021). AuthCODE: A privacy-preserving and multi-device continuous authentication architecture based on machine and deep learning. Computers and Security, 103:102168.

Abstract

The 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.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > General Computer Science
Social Sciences & Humanities > Law
Scope:Discipline-based scholarship (basic research)
Language:English
Date:April 2021
Deposited On:15 Mar 2022 08:19
Last Modified:18 Mar 2025 04:34
Publisher:Elsevier
ISSN:0167-4048
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.cose.2020.102168
Related URLs:https://doi.org/10.1016/j.cose.2020.102168
Other Identification Number:merlin-id:21888
Full text not available from this repository.

Metadata Export

Statistics

Citations

Dimensions.ai Metrics
18 citations in Web of Science®
12 citations in Scopus®
Google Scholar™

Altmetrics

Authors, Affiliations, Collaborations

Similar Publications