Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/MNET.2018.1800151 http://hdl.handle.net/11449/186701 |
Resumo: | Distinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the aforementioned context. The results are compared against one-class support vector machines and multivariate Gaussian distribution. Additionally, we also propose to employ meta-heuristic optimization techniques to fine-tune the OPF classifier in the context of anomaly detection in wireless sensor networks. |
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Intelligent Network Security Monitoring Based on Optimum-Path Forest ClusteringDistinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the aforementioned context. The results are compared against one-class support vector machines and multivariate Gaussian distribution. Additionally, we also propose to employ meta-heuristic optimization techniques to fine-tune the OPF classifier in the context of anomaly detection in wireless sensor networks.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FCT-Fundacao para a Ciencia e a TecnologiaFinepFuntel under Centro de Referencia em Radiocomunicacoes - CRR project of the Instituto Nacional de Telecomunicacoes (Inatel), Brazil.Fundação para o Desenvolvimento da UNESP (FUNDUNESP)Univ Fortaleza, Fortaleza, Ceara, BrazilUniv Fed Sao Carlos, Sao Carlos, SP, BrazilNatl Inst Telecommun Inatel, Lisbon, PortugalInst Telecomunicacoes, Lisbon, PortugalNatl Res Univ Higher Sch Econ, Dept Innovat & Business IT, Sch Business Informat, Fac Business & Management, Moscow, RussiaSao Paulo State Univ, Comp Sci Dept, Sao Paulo, BrazilSao Paulo State Univ, Comp Sci Dept, Sao Paulo, BrazilFAPESP: 2016/19403-6FAPESP: 2014/16250-9FAPESP: 2013/07375-0FAPESP: 2014/12236-1FAPESP: 309335/2017-5CNPq: 309335/2017-5CNPq: 304315/2017-6CNPq: 306166/2014-3CNPq: 307066/2017-7FCT-Fundacao para a Ciencia e a Tecnologia: UID/EEA/50008/2013Funtel under Centro de Referencia em Radiocomunicacoes - CRR project of the Instituto Nacional de Telecomunicacoes (Inatel), Brazil.: 01.14.0231.00FUNDUNESP: 2597.2017Ieee-inst Electrical Electronics Engineers IncUniv FortalezaUniversidade Federal de São Carlos (UFSCar)Natl Inst Telecommun InatelInst TelecomunicacoesNatl Res Univ Higher Sch EconUniversidade Estadual Paulista (Unesp)Guimaraes, Raniere RochaPassos Jr, Leandro A.Holanda Filho, RaimirAlbuquerque, Victor Hugo C. deRodrigues, Joel J. P. C.Komarov, Mikhail M.Papa, Joao Paulo [UNESP]2019-10-05T19:56:28Z2019-10-05T19:56:28Z2019-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article126-131http://dx.doi.org/10.1109/MNET.2018.1800151Ieee Network. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 33, n. 2, p. 126-131, 2019.0890-8044http://hdl.handle.net/11449/18670110.1109/MNET.2018.1800151WOS:000463036200018Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Networkinfo:eu-repo/semantics/openAccess2024-04-23T16:10:47Zoai:repositorio.unesp.br:11449/186701Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:10:47Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering |
title |
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering |
spellingShingle |
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering Guimaraes, Raniere Rocha |
title_short |
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering |
title_full |
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering |
title_fullStr |
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering |
title_full_unstemmed |
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering |
title_sort |
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering |
author |
Guimaraes, Raniere Rocha |
author_facet |
Guimaraes, Raniere Rocha Passos Jr, Leandro A. Holanda Filho, Raimir Albuquerque, Victor Hugo C. de Rodrigues, Joel J. P. C. Komarov, Mikhail M. Papa, Joao Paulo [UNESP] |
author_role |
author |
author2 |
Passos Jr, Leandro A. Holanda Filho, Raimir Albuquerque, Victor Hugo C. de Rodrigues, Joel J. P. C. Komarov, Mikhail M. Papa, Joao Paulo [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Univ Fortaleza Universidade Federal de São Carlos (UFSCar) Natl Inst Telecommun Inatel Inst Telecomunicacoes Natl Res Univ Higher Sch Econ Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Guimaraes, Raniere Rocha Passos Jr, Leandro A. Holanda Filho, Raimir Albuquerque, Victor Hugo C. de Rodrigues, Joel J. P. C. Komarov, Mikhail M. Papa, Joao Paulo [UNESP] |
description |
Distinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the aforementioned context. The results are compared against one-class support vector machines and multivariate Gaussian distribution. Additionally, we also propose to employ meta-heuristic optimization techniques to fine-tune the OPF classifier in the context of anomaly detection in wireless sensor networks. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-05T19:56:28Z 2019-10-05T19:56:28Z 2019-03-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/MNET.2018.1800151 Ieee Network. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 33, n. 2, p. 126-131, 2019. 0890-8044 http://hdl.handle.net/11449/186701 10.1109/MNET.2018.1800151 WOS:000463036200018 |
url |
http://dx.doi.org/10.1109/MNET.2018.1800151 http://hdl.handle.net/11449/186701 |
identifier_str_mv |
Ieee Network. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 33, n. 2, p. 126-131, 2019. 0890-8044 10.1109/MNET.2018.1800151 WOS:000463036200018 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Network |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
126-131 |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1797789659396308992 |