Keystroke Dynamics Recognition based on Personal Data: A Comparative Experimental Evaluation Implementing Reproducible Research
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics Engineers Inc.Date
2015-09-11Citation
10.1109/BTAS.2015.7358772
2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2015.
ISBN
978-147-998-776-4DOI
10.1109/BTAS.2015.7358772Funded by
A.M. is supported by a post-doctoral Juan de la Cierva contract by the Spanish MECD (JCI-2012-12357). M.F. was supported by a scholarship provided by University of Naples Federico II and Compagnia di San Paolo. This work has been partially supported by projects: Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM Telefónica.Project
Gobierno de España. TEC2012-34881; info:eu-repo/grantAgreement/EC/FP7/284989Editor's Version
http://dx.doi.org/10.1109/BTAS.2015.7358772Subjects
Biometrics; Experimental evaluation; Keystroke dynamics; Keystroke patterns; New applications; Reproducible research; Research communities; Source codes; Authentication; TelecomunicacionesNote
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. A. Morales, M. Falanga, J. Fierrez, C. Sansone and J. Ortega-Garcia, "Keystroke dynamics recognition based on personal data: A comparative experimental evaluation implementing reproducible research," 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), Arlington, VA, 2015, pp. 1-6. doi: 10.1109/BTAS.2015.7358772Rights
© 2015 IEEEAbstract
This work proposes a new benchmark for keystroke
dynamics recognition on the basis of fully reproducible
research. Instead of traditional authentication approaches
based on complex passwords, we propose a novel keystroke
recognition based on typing patterns from personal data.
We present a new database made up with the keystroke
patterns of 63 users and 7560 samples. The proposed
approach eliminates the necessity to memorize complex
passwords (something that we know) by replacing them by
personal data (something that we are). The results
encourage to further explore this new application scenario
and the availability of data and source code represent a
new valuable resource for the research community.
Files in this item
Google Scholar:Morales Moreno, Aythami
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Falanga, Mario
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Fiérrez Aguilar, Julián
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Sansone, Carlo
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Ortega García, Javier
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