An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorCzestochowa University of Technology-
Autor(es): dc.creatorLucas, Thiago Jose-
Autor(es): dc.creatorDa Costa, Kelton A. Pontara-
Autor(es): dc.creatorScherer, Rafal-
Autor(es): dc.creatorPapa, Joao Paulo-
Data de aceite: dc.date.accessioned2025-08-21T20:16:46Z-
Data de disponibilização: dc.date.available2025-08-21T20:16:46Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SMC53654.2022.9945239-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249410-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249410-
Descrição: dc.descriptionMachine learning techniques have achieved promising results in detecting attacks in computer networks, particularly ensemble learning methods, improving individual classifier's performance. This work focuses on building an ensemble of classifiers to minimize the computational cost to some extent. A diversity-driven pruning method was applied to create stackings using a combination of k-Nearest Neighbors, Decision Trees, Support Vector Machines, and Neural Networks, and validated on six differents datasets. An average accuracy of 99.94% and a reduction in the processing time of 97.34% are reported with heterogeneous ensembles, highlighting the robustness of the proposed approach.-
Descrição: dc.descriptionSão Paulo State University Department of Computing-
Descrição: dc.descriptionCzestochowa University of Technology-
Descrição: dc.descriptionSão Paulo State University Department of Computing-
Formato: dc.format1173-1179-
Idioma: dc.languageen-
Relação: dc.relationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectensemble learning-
Palavras-chave: dc.subjectensemble pruning-
Palavras-chave: dc.subjectintrusion detection-
Palavras-chave: dc.subjectstacking-
Título: dc.titleAn Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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