Machine Learning for Web Intrusion Detection: A Comparative Analysis of Feature Selection Methods mRMR and PFI

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorCollege of Technology-
Autor(es): dc.creatorLucas, Thiago José-
Autor(es): dc.creatorTojeiro, Carlos Alexandre Carvalho-
Autor(es): dc.creatorPires, Rafael Gonçalves-
Autor(es): dc.creatorda Costa, Kelton Augusto Pontara-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T17:58:13Z-
Data de disponibilização: dc.date.available2025-08-21T17:58:13Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-030-61401-0_50-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233059-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233059-
Descrição: dc.descriptionSelect from the best features in a complex dataset that is a critical task for machine learning algorithms. This work presents a comparative analysis between two resource selection techniques: Minimum Redundancy Maximum Relevance (mRMR) and Permutation Feature Important (PFI). The application of PFI to the dataset in issue is unusual. The dataset used in the experiments is HTTP CSIC 2010, which shows great results with the mRMR observed in a related work[22]. Our PFI tests resulted in a selection of features best suited for machine learning methods and the best results for an accuracy of 97% with logistic regression and Bayes Point Machine, 98% with Support Vector Machine, and 99.9% using an artificial neural network.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionCollege of Technology-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionFAPESP: 2013/07375-0-
Descrição: dc.descriptionFAPESP: 2014/12236-1-
Descrição: dc.descriptionFAPESP: 2017/22905-6-
Descrição: dc.descriptionFAPESP: 2019/07665-4-
Formato: dc.format535-546-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFeature selection-
Palavras-chave: dc.subjectIntrusion detection-
Palavras-chave: dc.subjectMachine learning-
Título: dc.titleMachine Learning for Web Intrusion Detection: A Comparative Analysis of Feature Selection Methods mRMR and PFI-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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