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...
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2021
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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) |
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Unsolicited electronic mail machine learning internet network security Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
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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 |
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