Study on Machine Learning Techniques for Botnet Detection
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TLA.2020.9082916 http://hdl.handle.net/11449/195369 |
Resumo: | This paper presents a study on the application of machine learning techniques for botnet detection, compromised computer networks controlled by an attacker in order to perform malicious activities, such as distributed denial-of-service attacks (DDoS), data theft and others. The study aims to evaluate the efficiency of commonly used classifiers in the literature for botnet traffic classification and, to this end, we compare the results obtained from each classifier using two different approaches for feature selection, the first one taking into account the most frequently used features in problems of this nature, based on previous works, and the second one taking into account features selected by the Recursive Feature Elimination algorithm, a relatively unexplored feature selection method in the botnet detection area. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Study on Machine Learning Techniques for Botnet DetectionBotnetMachine LearningRecursive Feature EliminationThis paper presents a study on the application of machine learning techniques for botnet detection, compromised computer networks controlled by an attacker in order to perform malicious activities, such as distributed denial-of-service attacks (DDoS), data theft and others. The study aims to evaluate the efficiency of commonly used classifiers in the literature for botnet traffic classification and, to this end, we compare the results obtained from each classifier using two different approaches for feature selection, the first one taking into account the most frequently used features in problems of this nature, based on previous works, and the second one taking into account features selected by the Recursive Feature Elimination algorithm, a relatively unexplored feature selection method in the botnet detection area.Univ Estadual Paulista, Bauru, SP, BrazilUniv Estadual Paulista, Bauru, SP, BrazilIeee-inst Electrical Electronics Engineers IncUniversidade Estadual Paulista (Unesp)Silva, L. [UNESP]Utimura, L. [UNESP]Costa, K. [UNESP]Silva, M. [UNESP]Prado, S. [UNESP]2020-12-10T17:32:09Z2020-12-10T17:32:09Z2020-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article881-888http://dx.doi.org/10.1109/TLA.2020.9082916Ieee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 18, n. 5, p. 881-888, 2020.1548-0992http://hdl.handle.net/11449/19536910.1109/TLA.2020.9082916WOS:000532329800009Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Latin America Transactionsinfo:eu-repo/semantics/openAccess2021-10-23T08:11:01Zoai:repositorio.unesp.br:11449/195369Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:07:54.608111Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Study on Machine Learning Techniques for Botnet Detection |
title |
Study on Machine Learning Techniques for Botnet Detection |
spellingShingle |
Study on Machine Learning Techniques for Botnet Detection Silva, L. [UNESP] Botnet Machine Learning Recursive Feature Elimination |
title_short |
Study on Machine Learning Techniques for Botnet Detection |
title_full |
Study on Machine Learning Techniques for Botnet Detection |
title_fullStr |
Study on Machine Learning Techniques for Botnet Detection |
title_full_unstemmed |
Study on Machine Learning Techniques for Botnet Detection |
title_sort |
Study on Machine Learning Techniques for Botnet Detection |
author |
Silva, L. [UNESP] |
author_facet |
Silva, L. [UNESP] Utimura, L. [UNESP] Costa, K. [UNESP] Silva, M. [UNESP] Prado, S. [UNESP] |
author_role |
author |
author2 |
Utimura, L. [UNESP] Costa, K. [UNESP] Silva, M. [UNESP] Prado, S. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Silva, L. [UNESP] Utimura, L. [UNESP] Costa, K. [UNESP] Silva, M. [UNESP] Prado, S. [UNESP] |
dc.subject.por.fl_str_mv |
Botnet Machine Learning Recursive Feature Elimination |
topic |
Botnet Machine Learning Recursive Feature Elimination |
description |
This paper presents a study on the application of machine learning techniques for botnet detection, compromised computer networks controlled by an attacker in order to perform malicious activities, such as distributed denial-of-service attacks (DDoS), data theft and others. The study aims to evaluate the efficiency of commonly used classifiers in the literature for botnet traffic classification and, to this end, we compare the results obtained from each classifier using two different approaches for feature selection, the first one taking into account the most frequently used features in problems of this nature, based on previous works, and the second one taking into account features selected by the Recursive Feature Elimination algorithm, a relatively unexplored feature selection method in the botnet detection area. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T17:32:09Z 2020-12-10T17:32:09Z 2020-05-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/TLA.2020.9082916 Ieee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 18, n. 5, p. 881-888, 2020. 1548-0992 http://hdl.handle.net/11449/195369 10.1109/TLA.2020.9082916 WOS:000532329800009 |
url |
http://dx.doi.org/10.1109/TLA.2020.9082916 http://hdl.handle.net/11449/195369 |
identifier_str_mv |
Ieee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 18, n. 5, p. 881-888, 2020. 1548-0992 10.1109/TLA.2020.9082916 WOS:000532329800009 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Latin America Transactions |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
881-888 |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129289524084736 |