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Authors: José Torres 1 ; Sergio De Los Santos 1 ; Efthimios Alepis 2 and Constantinos Patsakis 2

Affiliations: 1 Telefónica Digital España, Ronda de la Comunicación S/N, Madrid and Spain ; 2 Department of Informatics, University of Piraeus, Karaoli & Dimitriou 80, Piraeus and Greece

Keyword(s): Android, Authentication, Biometrics.

Abstract: The penetration of ICT in our everyday lives has introduced numerous automations, and the continuous need for communication has made mobile devices indispensable. As a result, there is an ever-increasing deployment of services for which users need to authenticate. While the use of plain passwords is the default, many applications require higher standards of security, such as drawn patterns and fingerprints, used mostly to authenticate users and unlock their smart devices. In this work we propose a biometrics-based machine learning approach that supports user authentication in Android to augment native user authentication mechanisms, making the process more seamless and secure. Our evaluation results show very high rates of success, both for authenticating the legitimate user and also for rejecting the false ones. Finally, we showcase how the proposed solution can be deployed in non-rooted devices.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Torres, J.; Santos, S.; Alepis, E. and Patsakis, C. (2019). Behavioral Biometric Authentication in Android Unlock Patterns through Machine Learning. In Proceedings of the 5th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-359-9; ISSN 2184-4356, SciTePress, pages 146-154. DOI: 10.5220/0007394201460154

@conference{icissp19,
author={José Torres. and Sergio De Los Santos. and Efthimios Alepis. and Constantinos Patsakis.},
title={Behavioral Biometric Authentication in Android Unlock Patterns through Machine Learning},
booktitle={Proceedings of the 5th International Conference on Information Systems Security and Privacy - ICISSP},
year={2019},
pages={146-154},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007394201460154},
isbn={978-989-758-359-9},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Information Systems Security and Privacy - ICISSP
TI - Behavioral Biometric Authentication in Android Unlock Patterns through Machine Learning
SN - 978-989-758-359-9
IS - 2184-4356
AU - Torres, J.
AU - Santos, S.
AU - Alepis, E.
AU - Patsakis, C.
PY - 2019
SP - 146
EP - 154
DO - 10.5220/0007394201460154
PB - SciTePress

- Science and Technology Publications, Lda.
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