A Study and Evaluation of Classifiers for Anti-Spam Systems

The volume of e-mails has been increasing in recent years. However, since 2005, at least half of these e-mails have been made up of spam. This massive traffic of unwanted messages causes losses to users, such as the excessive and unnecessary use of the bandwidth of their networks, loss of productivi...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Marcelo V. C. Aragao, Isaac C. Ferreira, Edvard M. Oliveira, Bruno T. Kuehne, Edmilson M. Moreira, Otavio A. S. Carpinteiro
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Acceso en línea:https://doaj.org/article/a35ad6e93d9045b8a99414311531d401
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:a35ad6e93d9045b8a99414311531d401
record_format dspace
spelling oai:doaj.org-article:a35ad6e93d9045b8a99414311531d4012021-12-03T00:00:55ZA Study and Evaluation of Classifiers for Anti-Spam Systems2169-353610.1109/ACCESS.2021.3129203https://doaj.org/article/a35ad6e93d9045b8a99414311531d4012021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9620064/https://doaj.org/toc/2169-3536The volume of e-mails has been increasing in recent years. However, since 2005, at least half of these e-mails have been made up of spam. This massive traffic of unwanted messages causes losses to users, such as the excessive and unnecessary use of the bandwidth of their networks, loss of productivity, exposure of inappropriate content to inappropriate audiences etc. This paper proposes the study and the application of machine learning models to the classification of e-mails in existing anti-spam systems and, in particular, in the new anti-spam system Open-MaLBAS. After carrying out many experiments on different data sets, it was possible both to prove the feasibility of the proposal and to develop a powerful combination of techniques, methods, and models that can be successfully applied to the classification of e-mails in anti-spam systems.Marcelo V. C. AragaoIsaac C. FerreiraEdvard M. OliveiraBruno T. KuehneEdmilson M. MoreiraOtavio A. S. CarpinteiroIEEEarticleUnsolicited electronic mailmachine learninginternetnetwork securityElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157482-157498 (2021)
institution DOAJ
collection DOAJ
language EN
topic Unsolicited electronic mail
machine learning
internet
network security
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Unsolicited electronic mail
machine learning
internet
network security
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Marcelo V. C. Aragao
Isaac C. Ferreira
Edvard M. Oliveira
Bruno T. Kuehne
Edmilson M. Moreira
Otavio A. S. Carpinteiro
A Study and Evaluation of Classifiers for Anti-Spam Systems
description The volume of e-mails has been increasing in recent years. However, since 2005, at least half of these e-mails have been made up of spam. This massive traffic of unwanted messages causes losses to users, such as the excessive and unnecessary use of the bandwidth of their networks, loss of productivity, exposure of inappropriate content to inappropriate audiences etc. This paper proposes the study and the application of machine learning models to the classification of e-mails in existing anti-spam systems and, in particular, in the new anti-spam system Open-MaLBAS. After carrying out many experiments on different data sets, it was possible both to prove the feasibility of the proposal and to develop a powerful combination of techniques, methods, and models that can be successfully applied to the classification of e-mails in anti-spam systems.
format article
author Marcelo V. C. Aragao
Isaac C. Ferreira
Edvard M. Oliveira
Bruno T. Kuehne
Edmilson M. Moreira
Otavio A. S. Carpinteiro
author_facet Marcelo V. C. Aragao
Isaac C. Ferreira
Edvard M. Oliveira
Bruno T. Kuehne
Edmilson M. Moreira
Otavio A. S. Carpinteiro
author_sort Marcelo V. C. Aragao
title A Study and Evaluation of Classifiers for Anti-Spam Systems
title_short A Study and Evaluation of Classifiers for Anti-Spam Systems
title_full A Study and Evaluation of Classifiers for Anti-Spam Systems
title_fullStr A Study and Evaluation of Classifiers for Anti-Spam Systems
title_full_unstemmed A Study and Evaluation of Classifiers for Anti-Spam Systems
title_sort study and evaluation of classifiers for anti-spam systems
publisher IEEE
publishDate 2021
url https://doaj.org/article/a35ad6e93d9045b8a99414311531d401
work_keys_str_mv AT marcelovcaragao astudyandevaluationofclassifiersforantispamsystems
AT isaaccferreira astudyandevaluationofclassifiersforantispamsystems
AT edvardmoliveira astudyandevaluationofclassifiersforantispamsystems
AT brunotkuehne astudyandevaluationofclassifiersforantispamsystems
AT edmilsonmmoreira astudyandevaluationofclassifiersforantispamsystems
AT otavioascarpinteiro astudyandevaluationofclassifiersforantispamsystems
AT marcelovcaragao studyandevaluationofclassifiersforantispamsystems
AT isaaccferreira studyandevaluationofclassifiersforantispamsystems
AT edvardmoliveira studyandevaluationofclassifiersforantispamsystems
AT brunotkuehne studyandevaluationofclassifiersforantispamsystems
AT edmilsonmmoreira studyandevaluationofclassifiersforantispamsystems
AT otavioascarpinteiro studyandevaluationofclassifiersforantispamsystems
_version_ 1718374006215671808