Study on Machine Learning Techniques for Botnet Detection

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorSilva, L. [UNESP]-
Autor(es): dc.creatorUtimura, L. [UNESP]-
Autor(es): dc.creatorCosta, K. [UNESP]-
Autor(es): dc.creatorSilva, M. [UNESP]-
Autor(es): dc.creatorPrado, S. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:05:38Z-
Data de disponibilização: dc.date.available2022-02-22T00:05:38Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TLA.2020.9082916-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/195369-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/195369-
Descrição: dc.descriptionThis 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.-
Descrição: dc.descriptionUniv Estadual Paulista, Bauru, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Bauru, SP, Brazil-
Formato: dc.format881-888-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-inst Electrical Electronics Engineers Inc-
Relação: dc.relationIeee Latin America Transactions-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectBotnet-
Palavras-chave: dc.subjectMachine Learning-
Palavras-chave: dc.subjectRecursive Feature Elimination-
Título: dc.titleStudy on Machine Learning Techniques for Botnet Detection-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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