User clustering based on keystroke dynamics

Autores
Bertacchini, Maximiliano; Benitez, Carlos; Fierens, Pablo I.
Año de publicación
2010
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The PAM clustering algorithm is applied on the Si6 keystroke dataset in order to identify sessions of the same users. A number of heuristical outlier lters based on statistical properties of keystroke latencies are proposed and run on the dataset. Di erent tests are performed varying the number of digraphs that compose each observation and its dimensionality, in order to verify the assumption that more data gives a better quality of clustering and to estimate the minimum required number of dimensions. The number of clusters is estimated through the silhouette algorithm. Resulting clustering accuracy is measured by means of the F-measure, showing the viability of user identi cation through keystroke analysis.
Presentado en el V Workshop Arquitectura, Redes y Sistemas Operativos (WARSO)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
System architectures
sistema operativo
keystroke
clustering
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/19359
Acceder
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network_name_str SEDICI (UNLP)
spelling User clustering based on keystroke dynamicsBertacchini, MaximilianoBenitez, CarlosFierens, Pablo I.Ciencias InformáticasSystem architecturessistema operativokeystrokeclusteringThe PAM clustering algorithm is applied on the Si6 keystroke dataset in order to identify sessions of the same users. A number of heuristical outlier lters based on statistical properties of keystroke latencies are proposed and run on the dataset. Di erent tests are performed varying the number of digraphs that compose each observation and its dimensionality, in order to verify the assumption that more data gives a better quality of clustering and to estimate the minimum required number of dimensions. The number of clusters is estimated through the silhouette algorithm. Resulting clustering accuracy is measured by means of the F-measure, showing the viability of user identi cation through keystroke analysis.Presentado en el V Workshop Arquitectura, Redes y Sistemas Operativos (WARSO)Red de Universidades con Carreras en Informática (RedUNCI)2010-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf832-841http://sedici.unlp.edu.ar/handle/10915/19359spainfo:eu-repo/semantics/altIdentifier/isbn/978-950-9474-49-9info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:35:26Zoai:sedici.unlp.edu.ar:10915/19359Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:35:26.86SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv User clustering based on keystroke dynamics
title User clustering based on keystroke dynamics
spellingShingle User clustering based on keystroke dynamics
Bertacchini, Maximiliano
Ciencias Informáticas
System architectures
sistema operativo
keystroke
clustering
title_short User clustering based on keystroke dynamics
title_full User clustering based on keystroke dynamics
title_fullStr User clustering based on keystroke dynamics
title_full_unstemmed User clustering based on keystroke dynamics
title_sort User clustering based on keystroke dynamics
dc.creator.none.fl_str_mv Bertacchini, Maximiliano
Benitez, Carlos
Fierens, Pablo I.
author Bertacchini, Maximiliano
author_facet Bertacchini, Maximiliano
Benitez, Carlos
Fierens, Pablo I.
author_role author
author2 Benitez, Carlos
Fierens, Pablo I.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
System architectures
sistema operativo
keystroke
clustering
topic Ciencias Informáticas
System architectures
sistema operativo
keystroke
clustering
dc.description.none.fl_txt_mv The PAM clustering algorithm is applied on the Si6 keystroke dataset in order to identify sessions of the same users. A number of heuristical outlier lters based on statistical properties of keystroke latencies are proposed and run on the dataset. Di erent tests are performed varying the number of digraphs that compose each observation and its dimensionality, in order to verify the assumption that more data gives a better quality of clustering and to estimate the minimum required number of dimensions. The number of clusters is estimated through the silhouette algorithm. Resulting clustering accuracy is measured by means of the F-measure, showing the viability of user identi cation through keystroke analysis.
Presentado en el V Workshop Arquitectura, Redes y Sistemas Operativos (WARSO)
Red de Universidades con Carreras en Informática (RedUNCI)
description The PAM clustering algorithm is applied on the Si6 keystroke dataset in order to identify sessions of the same users. A number of heuristical outlier lters based on statistical properties of keystroke latencies are proposed and run on the dataset. Di erent tests are performed varying the number of digraphs that compose each observation and its dimensionality, in order to verify the assumption that more data gives a better quality of clustering and to estimate the minimum required number of dimensions. The number of clusters is estimated through the silhouette algorithm. Resulting clustering accuracy is measured by means of the F-measure, showing the viability of user identi cation through keystroke analysis.
publishDate 2010
dc.date.none.fl_str_mv 2010-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/19359
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dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-9474-49-9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
832-841
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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