Identifying important characteristics in the KDD99 intrusion detection dataset by feature selection using a hybrid approach

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal University of Mato Grosso-
Autor(es): dc.contributorFederal Institute of Mato Grosso-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorPurdue University-
Autor(es): dc.creatorAraújo, Nelcileno-
Autor(es): dc.creatorDe Oliveira, Ruy-
Autor(es): dc.creatorFerreira, Ed'Wilson-
Autor(es): dc.creatorShinoda, Ailton Akira-
Autor(es): dc.creatorBhargava, Bharat-
Data de aceite: dc.date.accessioned2025-08-21T21:40:25Z-
Data de disponibilização: dc.date.available2025-08-21T21:40:25Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2010-07-19-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICTEL.2010.5478852-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/225968-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/225968-
Descrição: dc.descriptionIntrusion detection datasets play a key role in fine tuning Intrusion Detection Systems (IDSs). Using such datasets one can distinguish between regular and anomalous behavior of a given node in the network. To build this dataset is not straightforward, though, as only the most significant features of the collected data for detecting the node's behavior should be considered. We propose in this paper a technique for selecting relevant features out of KDD99 using a hybrid approach toward an optimal subset of features. Unlike existing work that only detect attack or no attack conditions, our approach efficiently identifies which sort of attack each register in the dataset refers to. The evaluation results show that the optimized subset of features can improve performance of typical IDSs. © 2009 IEEE.-
Descrição: dc.descriptionInstitute of Computing Federal University of Mato Grosso, Cuiabá, MT-
Descrição: dc.descriptionDepartment of Informatics Federal Institute of Mato Grosso, Cuiabá, MT-
Descrição: dc.descriptionDepartment of Electrical Engineering State University Júlio de Mesquita Filho, Ilha Solteira, SP-
Descrição: dc.descriptionDepartment of Computer Science Purdue University, West Lafayette, IN-
Descrição: dc.descriptionDepartment of Electrical Engineering State University Júlio de Mesquita Filho, Ilha Solteira, SP-
Formato: dc.format552-558-
Idioma: dc.languageen-
Relação: dc.relationICT 2010: 2010 17th International Conference on Telecommunications-
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Palavras-chave: dc.subjectHybrid approach-
Palavras-chave: dc.subjectInformation gain ratio-
Palavras-chave: dc.subjectK-means-
Palavras-chave: dc.subjectKDD99. feature selection-
Título: dc.titleIdentifying important characteristics in the KDD99 intrusion detection dataset by feature selection using a hybrid approach-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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