3D Network Traffic Monitoring Based on an Automatic Attack Classifier
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , , , , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/184752 |
Resumo: | In the last years, the exponential growth of computer networks has created an incredibly increase of network data traffic. The management becomes a challenging task, requesting a continuous monitoring of the network to detect and diagnose problems, and to fix problems and to optimize performance. Tools, such as Tcpdump and Snort are commonly used as network sniffer, logging and analysis applied on a dedicated host or network segment. They capture the traffic and analyze it for suspicious usage patterns, such as those that occur normally with port scans or Denial-of-service attacks. These tools are very important for the network management, but they do not take advantage of human cognitive capacity of the learning and pattern recognition. To overcome this limitation, this paper aims to present a visual interactive and multiprojection 3D tool with automatic data classification for attack detection. |
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Repositório Institucional da UNESP |
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spelling |
3D Network Traffic Monitoring Based on an Automatic Attack ClassifierIn the last years, the exponential growth of computer networks has created an incredibly increase of network data traffic. The management becomes a challenging task, requesting a continuous monitoring of the network to detect and diagnose problems, and to fix problems and to optimize performance. Tools, such as Tcpdump and Snort are commonly used as network sniffer, logging and analysis applied on a dedicated host or network segment. They capture the traffic and analyze it for suspicious usage patterns, such as those that occur normally with port scans or Denial-of-service attacks. These tools are very important for the network management, but they do not take advantage of human cognitive capacity of the learning and pattern recognition. To overcome this limitation, this paper aims to present a visual interactive and multiprojection 3D tool with automatic data classification for attack detection.Univ Fed Sao Carlos, Dept Comp Sci, BR-13560 Sao Carlos, SP, BrazilUNESP, Comp Sci Dept, Bauru, SP, BrazilCorollarium Technol, Sao Paulo, SP, BrazilOpen Univ Brazil, Fed Univ Sao Paulo Faccamps Mast, Sao Paulo, SP, BrazilUNESP, Comp Sci Dept, Bauru, SP, BrazilSpringerUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Corollarium TechnolUniversidade de São Paulo (USP)Colombo Dias, Diego RobertoFerreira Brega, Jose Remo [UNESP]Trevelin, Luis CarlosGnecco, Bruno BarberiPapa, Joao Paulo [UNESP]Guimaraes, Marcelo de PaivaMurgante, B.Misra, S.Rocha, AMACTorre, C.Rocha, J. G.Falcao, M. I.Taniar, D.Apduhan, B. O.Gervasi, O.2019-10-04T12:29:44Z2019-10-04T12:29:44Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject342-+Computational Science And Its Applications - Iccsa 2014, Pt Ii. Berlin: Springer-verlag Berlin, v. 8580, p. 342-+, 2014.0302-9743http://hdl.handle.net/11449/184752WOS:000349532500026Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputational Science And Its Applications - Iccsa 2014, Pt Iiinfo:eu-repo/semantics/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/184752Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:49:31.218884Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
3D Network Traffic Monitoring Based on an Automatic Attack Classifier |
title |
3D Network Traffic Monitoring Based on an Automatic Attack Classifier |
spellingShingle |
3D Network Traffic Monitoring Based on an Automatic Attack Classifier Colombo Dias, Diego Roberto |
title_short |
3D Network Traffic Monitoring Based on an Automatic Attack Classifier |
title_full |
3D Network Traffic Monitoring Based on an Automatic Attack Classifier |
title_fullStr |
3D Network Traffic Monitoring Based on an Automatic Attack Classifier |
title_full_unstemmed |
3D Network Traffic Monitoring Based on an Automatic Attack Classifier |
title_sort |
3D Network Traffic Monitoring Based on an Automatic Attack Classifier |
author |
Colombo Dias, Diego Roberto |
author_facet |
Colombo Dias, Diego Roberto Ferreira Brega, Jose Remo [UNESP] Trevelin, Luis Carlos Gnecco, Bruno Barberi Papa, Joao Paulo [UNESP] Guimaraes, Marcelo de Paiva Murgante, B. Misra, S. Rocha, AMAC Torre, C. Rocha, J. G. Falcao, M. I. Taniar, D. Apduhan, B. O. Gervasi, O. |
author_role |
author |
author2 |
Ferreira Brega, Jose Remo [UNESP] Trevelin, Luis Carlos Gnecco, Bruno Barberi Papa, Joao Paulo [UNESP] Guimaraes, Marcelo de Paiva Murgante, B. Misra, S. Rocha, AMAC Torre, C. Rocha, J. G. Falcao, M. I. Taniar, D. Apduhan, B. O. Gervasi, O. |
author2_role |
author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) Corollarium Technol Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Colombo Dias, Diego Roberto Ferreira Brega, Jose Remo [UNESP] Trevelin, Luis Carlos Gnecco, Bruno Barberi Papa, Joao Paulo [UNESP] Guimaraes, Marcelo de Paiva Murgante, B. Misra, S. Rocha, AMAC Torre, C. Rocha, J. G. Falcao, M. I. Taniar, D. Apduhan, B. O. Gervasi, O. |
description |
In the last years, the exponential growth of computer networks has created an incredibly increase of network data traffic. The management becomes a challenging task, requesting a continuous monitoring of the network to detect and diagnose problems, and to fix problems and to optimize performance. Tools, such as Tcpdump and Snort are commonly used as network sniffer, logging and analysis applied on a dedicated host or network segment. They capture the traffic and analyze it for suspicious usage patterns, such as those that occur normally with port scans or Denial-of-service attacks. These tools are very important for the network management, but they do not take advantage of human cognitive capacity of the learning and pattern recognition. To overcome this limitation, this paper aims to present a visual interactive and multiprojection 3D tool with automatic data classification for attack detection. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01 2019-10-04T12:29:44Z 2019-10-04T12:29:44Z |
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 |
Computational Science And Its Applications - Iccsa 2014, Pt Ii. Berlin: Springer-verlag Berlin, v. 8580, p. 342-+, 2014. 0302-9743 http://hdl.handle.net/11449/184752 WOS:000349532500026 |
identifier_str_mv |
Computational Science And Its Applications - Iccsa 2014, Pt Ii. Berlin: Springer-verlag Berlin, v. 8580, p. 342-+, 2014. 0302-9743 WOS:000349532500026 |
url |
http://hdl.handle.net/11449/184752 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computational Science And Its Applications - Iccsa 2014, Pt Ii |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
342-+ |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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 |
|
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1808128568493867008 |