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:	Unsolicited electronic mail machine learning internet network security Electrical engineering. Electronics. Nuclear engineering TK1-9971
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

A Study and Evaluation of Classifiers for Anti-Spam Systems

Ejemplares similares