Optimum-path forest stacking-based ensemble for intrusion detection

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorBertoni, Mateus A. [UNESP]-
Autor(es): dc.creatorRosa, Gustavo H. de [UNESP]-
Autor(es): dc.creatorBrega, Jose R. F. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:56:20Z-
Data de disponibilização: dc.date.available2022-02-22T00:56:20Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-05-12-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s12065-021-00609-7-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/209385-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/209385-
Descrição: dc.descriptionMachine learning techniques have been extensively researched in the last years, mainly due to their effectiveness when dealing with recognition or classification applications. Typically, one can comprehend using a Machine Learning system to autonomously delegate routines, save human efforts, and produce great insights regarding decision-making tasks. This paper introduces and validates a stacking-based ensemble approach using Optimum-Path Forest classifiers in intrusion detection tasks. Instead of only using the famous NSL-KDD dataset, we propose a new dataset called uneSPY, which we believe will fill the gap concerning new intrusion detection datasets. Both datasets were evaluated under several classifiers, including Logistic Regression, Decision Trees, Support Vector Machines, Optimum-Path Forests, and compared against Optimum-Path Forest stacking-based ensembles. Experimental results showed an Optimum-Path Forest stacking-based ensemble classification suitability, particularly when considering its ability to generalize large volumes of data while sustaining its performance.-
Descrição: dc.descriptionSao Paulo State Univ, Sch Sci, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Sch Sci, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, Brazil-
Formato: dc.format18-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationEvolutionary Intelligence-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectOptimum-path forest-
Palavras-chave: dc.subjectEnsemble-
Palavras-chave: dc.subjectStacking-based ensemble-
Palavras-chave: dc.subjectIntrusion detection-
Título: dc.titleOptimum-path forest stacking-based ensemble for intrusion detection-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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