Datamining keystroke based biometrics database using rough sets

Detalhes bibliográficos
Autor(a) principal: Revett, Kenneth
Data de Publicação: 2005
Outros Autores: Magalhães, Paulo Sérgio Tenreiro, Santos, Henrique Dinis dos
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/1822/4292
Resumo: Software based biometrics, utilising keystroke dynamics has been proposed as a cost effective means of enhancing computer access security. Keystroke dynamics has been successfully employed as a means of identifying legitimate/illegitimate login attempts based on the typing style of the login entry. In this paper, we collected keystroke dynamics data in the form of digraphs from a series of users entering a specific login ID. We wished to determine if there were any particular patterns in the typing styles that would indicate whether a login attempt was legitimate or not using rough sets. Our analysis produced a sensitivity of 96%, specificity of 93% and an overall accuracy of 95%. The results of this study indicate that typing speed and the first few and the last few characters of the login ID were the most important indicators of whether the login attempt was legitimate or not.
id RCAP_7c4618897952e937a2c2d11c5a06c78b
oai_identifier_str oai:repositorium.sdum.uminho.pt:1822/4292
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Datamining keystroke based biometrics database using rough setsArtificial intelligenceDecision support systemsGenetic algorithmsSoftware based biometrics, utilising keystroke dynamics has been proposed as a cost effective means of enhancing computer access security. Keystroke dynamics has been successfully employed as a means of identifying legitimate/illegitimate login attempts based on the typing style of the login entry. In this paper, we collected keystroke dynamics data in the form of digraphs from a series of users entering a specific login ID. We wished to determine if there were any particular patterns in the typing styles that would indicate whether a login attempt was legitimate or not using rough sets. Our analysis produced a sensitivity of 96%, specificity of 93% and an overall accuracy of 95%. The results of this study indicate that typing speed and the first few and the last few characters of the login ID were the most important indicators of whether the login attempt was legitimate or not.IEEEUniversidade do MinhoRevett, KennethMagalhães, Paulo Sérgio TenreiroSantos, Henrique Dinis dos20052005-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/4292engWORKSHOP ON EXTRACTION OF KNOWLEDGE FROM DATABASES AND WAREHOUSES, Covilhã, Portugal, 2005 – “Workshop on Extraction of Knowledge from Databases and Warehouses: proceedings”. New York: IEEE, 2005. ISBN 0-7803-9365-1.0-7803-9365-1info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T06:49:38Zoai:repositorium.sdum.uminho.pt:1822/4292Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:05:47.312324Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Datamining keystroke based biometrics database using rough sets
title Datamining keystroke based biometrics database using rough sets
spellingShingle Datamining keystroke based biometrics database using rough sets
Revett, Kenneth
Artificial intelligence
Decision support systems
Genetic algorithms
title_short Datamining keystroke based biometrics database using rough sets
title_full Datamining keystroke based biometrics database using rough sets
title_fullStr Datamining keystroke based biometrics database using rough sets
title_full_unstemmed Datamining keystroke based biometrics database using rough sets
title_sort Datamining keystroke based biometrics database using rough sets
author Revett, Kenneth
author_facet Revett, Kenneth
Magalhães, Paulo Sérgio Tenreiro
Santos, Henrique Dinis dos
author_role author
author2 Magalhães, Paulo Sérgio Tenreiro
Santos, Henrique Dinis dos
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Revett, Kenneth
Magalhães, Paulo Sérgio Tenreiro
Santos, Henrique Dinis dos
dc.subject.por.fl_str_mv Artificial intelligence
Decision support systems
Genetic algorithms
topic Artificial intelligence
Decision support systems
Genetic algorithms
description Software based biometrics, utilising keystroke dynamics has been proposed as a cost effective means of enhancing computer access security. Keystroke dynamics has been successfully employed as a means of identifying legitimate/illegitimate login attempts based on the typing style of the login entry. In this paper, we collected keystroke dynamics data in the form of digraphs from a series of users entering a specific login ID. We wished to determine if there were any particular patterns in the typing styles that would indicate whether a login attempt was legitimate or not using rough sets. Our analysis produced a sensitivity of 96%, specificity of 93% and an overall accuracy of 95%. The results of this study indicate that typing speed and the first few and the last few characters of the login ID were the most important indicators of whether the login attempt was legitimate or not.
publishDate 2005
dc.date.none.fl_str_mv 2005
2005-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/4292
url http://hdl.handle.net/1822/4292
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv WORKSHOP ON EXTRACTION OF KNOWLEDGE FROM DATABASES AND WAREHOUSES, Covilhã, Portugal, 2005 – “Workshop on Extraction of Knowledge from Databases and Warehouses: proceedings”. New York: IEEE, 2005. ISBN 0-7803-9365-1.
0-7803-9365-1
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833595732603437056