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Mobile Keystroke Biometrics Using Transformers

Stragapede, Giuseppe, Delgado-Santos, Paula, Tolosana, Ruben, Vera-Rodriguez, Ruben, Guest, Richard, Morales, Aythami (2023) Mobile Keystroke Biometrics Using Transformers. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG). . IEEE ISBN 979-83-503-4545-2. E-ISBN 979-83-503-4544-5. (doi:10.1109/fg57933.2023.10042710) (KAR id:100281)

Abstract

Among user authentication methods, behavioural biometrics has proven to be effective against identity theft as well as user-friendly and unobtrusive. One of the most popular traits in the literature is keystroke dynamics due to the large deployment of computers and mobile devices in our society. This paper focuses on improving keystroke biometric systems on the free-text scenario. This scenario is characterised as very challenging due to the uncontrolled text conditions, the influence of the user's emotional and physical state, and the in-use application. To overcome these drawbacks, methods based on deep learning such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been proposed in the literature, outperforming traditional machine learning methods. However, these architectures still have aspects that need to be reviewed and improved. To the best of our knowl-edge, this is the first study that proposes keystroke biometric systems based on Transformers. The proposed Transformer architecture has achieved Equal Error Rate (EER) values of 3.84% in the popular Aalto mobile keystroke database using only 5 enrolment sessions, outperforming by a large margin other state-of-the-art approaches in the literature.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/fg57933.2023.10042710
Additional information: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 10 Mar 2023 15:56 UTC
Last Modified: 13 Mar 2023 15:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/100281 (The current URI for this page, for reference purposes)

University of Kent Author Information

Delgado-Santos, Paula.

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Guest, Richard.

Creator's ORCID: https://orcid.org/0000-0001-7535-7336
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