Intrusion Detection System Based on Flows Using Machine Learning Algorithms
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TLA.2017.8071245 http://hdl.handle.net/11449/179313 |
Resumo: | The use of technology information and communication by different types of devices generates a large quantity of data packets that contains of confidential and personal information. The traffic of data packet can be summarized in network flow. Due this reason, it is necessary to use computer security tools, such as Intrusion Detection Systems (IDS). This work presents an IDS that can perform the flow- based analysis (netflow). This research conducted an analysis on flows previously collected and properly detected of three different types of attacks. The flows were organized to be processed by machine learning methods. The results obtained by proposed approach were very promising. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster IDS-related research. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Intrusion Detection System Based on Flows Using Machine Learning AlgorithmsBayes ClassifierIntrusion Detection SystemKNNMachine LearningNetflowOPFSVMThe use of technology information and communication by different types of devices generates a large quantity of data packets that contains of confidential and personal information. The traffic of data packet can be summarized in network flow. Due this reason, it is necessary to use computer security tools, such as Intrusion Detection Systems (IDS). This work presents an IDS that can perform the flow- based analysis (netflow). This research conducted an analysis on flows previously collected and properly detected of three different types of attacks. The flows were organized to be processed by machine learning methods. The results obtained by proposed approach were very promising. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster IDS-related research.Universidade Do Oeste Paulista (Unoeste)Universidade Estadual Paulista (Unesp)Universidade de Fortaleza (Unifor)Universidade Estadual Paulista (Unesp)Universidade Do Oeste Paulista (Unoeste)Universidade Estadual Paulista (Unesp)Universidade de Fortaleza (Unifor)Kakihata, Eduardo MassatoSapia, Helton MolinaOiakawa, Ronaldo ToshiakiPereira, Danillo RobertoPapa, Joao Paulo [UNESP]De Albuquerque, Victor Hugo CostaDa Silva, Francisco Assis2018-12-11T17:34:40Z2018-12-11T17:34:40Z2017-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1988-1993application/pdfhttp://dx.doi.org/10.1109/TLA.2017.8071245IEEE Latin America Transactions, v. 15, n. 10, p. 1988-1993, 2017.1548-0992http://hdl.handle.net/11449/17931310.1109/TLA.2017.80712452-s2.0-850326166662-s2.0-85032616666.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporIEEE Latin America Transactions0,253info:eu-repo/semantics/openAccess2024-04-23T16:11:00Zoai:repositorio.unesp.br:11449/179313Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:52:08.231323Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Intrusion Detection System Based on Flows Using Machine Learning Algorithms |
title |
Intrusion Detection System Based on Flows Using Machine Learning Algorithms |
spellingShingle |
Intrusion Detection System Based on Flows Using Machine Learning Algorithms Kakihata, Eduardo Massato Bayes Classifier Intrusion Detection System KNN Machine Learning Netflow OPF SVM |
title_short |
Intrusion Detection System Based on Flows Using Machine Learning Algorithms |
title_full |
Intrusion Detection System Based on Flows Using Machine Learning Algorithms |
title_fullStr |
Intrusion Detection System Based on Flows Using Machine Learning Algorithms |
title_full_unstemmed |
Intrusion Detection System Based on Flows Using Machine Learning Algorithms |
title_sort |
Intrusion Detection System Based on Flows Using Machine Learning Algorithms |
author |
Kakihata, Eduardo Massato |
author_facet |
Kakihata, Eduardo Massato Sapia, Helton Molina Oiakawa, Ronaldo Toshiaki Pereira, Danillo Roberto Papa, Joao Paulo [UNESP] De Albuquerque, Victor Hugo Costa Da Silva, Francisco Assis |
author_role |
author |
author2 |
Sapia, Helton Molina Oiakawa, Ronaldo Toshiaki Pereira, Danillo Roberto Papa, Joao Paulo [UNESP] De Albuquerque, Victor Hugo Costa Da Silva, Francisco Assis |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Do Oeste Paulista (Unoeste) Universidade Estadual Paulista (Unesp) Universidade de Fortaleza (Unifor) |
dc.contributor.author.fl_str_mv |
Kakihata, Eduardo Massato Sapia, Helton Molina Oiakawa, Ronaldo Toshiaki Pereira, Danillo Roberto Papa, Joao Paulo [UNESP] De Albuquerque, Victor Hugo Costa Da Silva, Francisco Assis |
dc.subject.por.fl_str_mv |
Bayes Classifier Intrusion Detection System KNN Machine Learning Netflow OPF SVM |
topic |
Bayes Classifier Intrusion Detection System KNN Machine Learning Netflow OPF SVM |
description |
The use of technology information and communication by different types of devices generates a large quantity of data packets that contains of confidential and personal information. The traffic of data packet can be summarized in network flow. Due this reason, it is necessary to use computer security tools, such as Intrusion Detection Systems (IDS). This work presents an IDS that can perform the flow- based analysis (netflow). This research conducted an analysis on flows previously collected and properly detected of three different types of attacks. The flows were organized to be processed by machine learning methods. The results obtained by proposed approach were very promising. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster IDS-related research. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-10-01 2018-12-11T17:34:40Z 2018-12-11T17:34:40Z |
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/TLA.2017.8071245 IEEE Latin America Transactions, v. 15, n. 10, p. 1988-1993, 2017. 1548-0992 http://hdl.handle.net/11449/179313 10.1109/TLA.2017.8071245 2-s2.0-85032616666 2-s2.0-85032616666.pdf |
url |
http://dx.doi.org/10.1109/TLA.2017.8071245 http://hdl.handle.net/11449/179313 |
identifier_str_mv |
IEEE Latin America Transactions, v. 15, n. 10, p. 1988-1993, 2017. 1548-0992 10.1109/TLA.2017.8071245 2-s2.0-85032616666 2-s2.0-85032616666.pdf |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
IEEE Latin America Transactions 0,253 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1988-1993 application/pdf |
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_ |
1808129131336957952 |