Intrusion Detection System Based on Flows Using Machine Learning Algorithms

Detalhes bibliográficos
Autor(a) principal: Kakihata, Eduardo Massato
Data de Publicação: 2017
Outros Autores: Sapia, Helton Molina, Oiakawa, Ronaldo Toshiaki, Pereira, Danillo Roberto, Papa, Joao Paulo [UNESP], De Albuquerque, Victor Hugo Costa, Da Silva, Francisco Assis
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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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
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