Learning to detect spam messages

Autores
Gil Costa, Graciela Verónica; Errecalde, Marcelo Luis; Taranilla, María Teresa
Año de publicación
2005
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques. In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested. Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts.
VI Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Electronic mail
Message sending
Information filtering
spam
anti-spam filtering
automated text categorization
machine learning
k-NN
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:10915/22957

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network_name_str SEDICI (UNLP)
spelling Learning to detect spam messagesGil Costa, Graciela VerónicaErrecalde, Marcelo LuisTaranilla, María TeresaCiencias InformáticasElectronic mailMessage sendingInformation filteringspamanti-spam filteringautomated text categorizationmachine learningk-NNThe problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques. In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested. Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts.VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2005-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/22957enginfo: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:UNLP2025-09-29T10:55:16Zoai:sedici.unlp.edu.ar:10915/22957Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:16.902SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Learning to detect spam messages
title Learning to detect spam messages
spellingShingle Learning to detect spam messages
Gil Costa, Graciela Verónica
Ciencias Informáticas
Electronic mail
Message sending
Information filtering
spam
anti-spam filtering
automated text categorization
machine learning
k-NN
title_short Learning to detect spam messages
title_full Learning to detect spam messages
title_fullStr Learning to detect spam messages
title_full_unstemmed Learning to detect spam messages
title_sort Learning to detect spam messages
dc.creator.none.fl_str_mv Gil Costa, Graciela Verónica
Errecalde, Marcelo Luis
Taranilla, María Teresa
author Gil Costa, Graciela Verónica
author_facet Gil Costa, Graciela Verónica
Errecalde, Marcelo Luis
Taranilla, María Teresa
author_role author
author2 Errecalde, Marcelo Luis
Taranilla, María Teresa
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Electronic mail
Message sending
Information filtering
spam
anti-spam filtering
automated text categorization
machine learning
k-NN
topic Ciencias Informáticas
Electronic mail
Message sending
Information filtering
spam
anti-spam filtering
automated text categorization
machine learning
k-NN
dc.description.none.fl_txt_mv The problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques. In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested. Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts.
VI Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description The problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques. In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested. Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts.
publishDate 2005
dc.date.none.fl_str_mv 2005-10
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
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22957
url http://sedici.unlp.edu.ar/handle/10915/22957
dc.language.none.fl_str_mv eng
language eng
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)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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