Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions
This study introduces the Live Spam Beater (LiSB) framework for the execution of email filtering techniques during SMTP (Simple Mail Transfer Protocol) transactions. It aims to increase the effectiveness and efficiency of existing proactive filtering mechanisms, mainly based on simple blacklists. Si...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/20/10491 |
| _version_ | 1827651788843515904 |
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| author | Silvana Gómez-Meire César Gabriel Márquez Eliana Patricia Aray-Cappello José R. Méndez |
| author_facet | Silvana Gómez-Meire César Gabriel Márquez Eliana Patricia Aray-Cappello José R. Méndez |
| author_sort | Silvana Gómez-Meire |
| collection | DOAJ |
| description | This study introduces the Live Spam Beater (LiSB) framework for the execution of email filtering techniques during SMTP (Simple Mail Transfer Protocol) transactions. It aims to increase the effectiveness and efficiency of existing proactive filtering mechanisms, mainly based on simple blacklists. Since it implements some proactive filtering schemes (during SMTP transaction), when an email message is classified as spam, the sender can be notified by an SMTP response code as a result of the transaction itself. The presented framework is written in Python programming language, works as an MTA (Mail Transfer Agent) server that implements an SMTP (Simple Mail Transfer Protocol) reverse proxy and allows the use of plugins to easily incorporate new filtering techniques designed to operate proactively. We also include a plugin to perform proactive content-based filtering through the analysis of words included in the body of the email message. Finally, we measured the performance of the plugin and the framework (time required for operation and accuracy) obtaining values suitable for their use during SMTP transactions. |
| first_indexed | 2024-03-09T20:46:28Z |
| format | Article |
| id | doaj.art-3872cb171214494ea36b1f879cedadf1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:46:28Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3872cb171214494ea36b1f879cedadf12023-11-23T22:45:35ZengMDPI AGApplied Sciences2076-34172022-10-0112201049110.3390/app122010491Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP TransactionsSilvana Gómez-Meire0César Gabriel Márquez1Eliana Patricia Aray-Cappello2José R. Méndez3Department of Computer Science, Universidade de Vigo, ESEI—Escola Superior de Enxeñaría Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, 32004 Ourense, SpainDepartment of Computer Science, Universidade de Vigo, ESEI—Escola Superior de Enxeñaría Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, 32004 Ourense, SpainDepartment of Computer Science, Universidade de Vigo, ESEI—Escola Superior de Enxeñaría Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, 32004 Ourense, SpainDepartment of Computer Science, Universidade de Vigo, ESEI—Escola Superior de Enxeñaría Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, 32004 Ourense, SpainThis study introduces the Live Spam Beater (LiSB) framework for the execution of email filtering techniques during SMTP (Simple Mail Transfer Protocol) transactions. It aims to increase the effectiveness and efficiency of existing proactive filtering mechanisms, mainly based on simple blacklists. Since it implements some proactive filtering schemes (during SMTP transaction), when an email message is classified as spam, the sender can be notified by an SMTP response code as a result of the transaction itself. The presented framework is written in Python programming language, works as an MTA (Mail Transfer Agent) server that implements an SMTP (Simple Mail Transfer Protocol) reverse proxy and allows the use of plugins to easily incorporate new filtering techniques designed to operate proactively. We also include a plugin to perform proactive content-based filtering through the analysis of words included in the body of the email message. Finally, we measured the performance of the plugin and the framework (time required for operation and accuracy) obtaining values suitable for their use during SMTP transactions.https://www.mdpi.com/2076-3417/12/20/10491spam filteringproactive spam filteringSMTP transactionsmachine learningprofile-based filteringanomaly detection |
| spellingShingle | Silvana Gómez-Meire César Gabriel Márquez Eliana Patricia Aray-Cappello José R. Méndez Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions Applied Sciences spam filtering proactive spam filtering SMTP transactions machine learning profile-based filtering anomaly detection |
| title | Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions |
| title_full | Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions |
| title_fullStr | Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions |
| title_full_unstemmed | Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions |
| title_short | Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions |
| title_sort | using live spam beater lisb framework for spam filtering during smtp transactions |
| topic | spam filtering proactive spam filtering SMTP transactions machine learning profile-based filtering anomaly detection |
| url | https://www.mdpi.com/2076-3417/12/20/10491 |
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