An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/SMC53654.2022.9945239 http://hdl.handle.net/11449/249410 |
Resumo: | Machine 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. |
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Repositório Institucional da UNESP |
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An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performanceensemble learningensemble pruningintrusion detectionstackingMachine 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.São Paulo State University Department of ComputingCzestochowa University of TechnologySão Paulo State University Department of ComputingUniversidade Estadual Paulista (UNESP)Czestochowa University of TechnologyLucas, Thiago Jose [UNESP]Da Costa, Kelton A. Pontara [UNESP]Scherer, RafalPapa, Joao Paulo [UNESP]2023-07-29T15:15:20Z2023-07-29T15:15:20Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1173-1179http://dx.doi.org/10.1109/SMC53654.2022.9945239Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, v. 2022-October, p. 1173-1179.1062-922Xhttp://hdl.handle.net/11449/24941010.1109/SMC53654.2022.99452392-s2.0-85142755955Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengConference Proceedings - IEEE International Conference on Systems, Man and Cyberneticsinfo:eu-repo/semantics/openAccess2024-04-23T16:11:27Zoai:repositorio.unesp.br:11449/249410Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:02:11.194396Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance |
title |
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance |
spellingShingle |
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance Lucas, Thiago Jose [UNESP] ensemble learning ensemble pruning intrusion detection stacking |
title_short |
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance |
title_full |
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance |
title_fullStr |
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance |
title_full_unstemmed |
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance |
title_sort |
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance |
author |
Lucas, Thiago Jose [UNESP] |
author_facet |
Lucas, Thiago Jose [UNESP] Da Costa, Kelton A. Pontara [UNESP] Scherer, Rafal Papa, Joao Paulo [UNESP] |
author_role |
author |
author2 |
Da Costa, Kelton A. Pontara [UNESP] Scherer, Rafal Papa, Joao Paulo [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Czestochowa University of Technology |
dc.contributor.author.fl_str_mv |
Lucas, Thiago Jose [UNESP] Da Costa, Kelton A. Pontara [UNESP] Scherer, Rafal Papa, Joao Paulo [UNESP] |
dc.subject.por.fl_str_mv |
ensemble learning ensemble pruning intrusion detection stacking |
topic |
ensemble learning ensemble pruning intrusion detection stacking |
description |
Machine 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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-07-29T15:15:20Z 2023-07-29T15:15:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/SMC53654.2022.9945239 Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, v. 2022-October, p. 1173-1179. 1062-922X http://hdl.handle.net/11449/249410 10.1109/SMC53654.2022.9945239 2-s2.0-85142755955 |
url |
http://dx.doi.org/10.1109/SMC53654.2022.9945239 http://hdl.handle.net/11449/249410 |
identifier_str_mv |
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, v. 2022-October, p. 1173-1179. 1062-922X 10.1109/SMC53654.2022.9945239 2-s2.0-85142755955 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1173-1179 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129152789774336 |