Datamining keystroke based biometrics database using rough sets
Autor(a) principal: | |
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Data de Publicação: | 2005 |
Outros Autores: | , |
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. |
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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 |
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FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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RCAAP |
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RCAAP |
reponame_str |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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info@rcaap.pt |
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1833595732603437056 |