Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques

Network anomaly detection is currently a challenge due to the number of different attacks and the number of potential attackers. Intrusion detection systems aim to detect misuses or network anomalies in order to block ports or connections, whereas firewalls act according to a predefined set of rules...

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Autores:
De La Hoz, Eduardo
Ortiz, Andrés
Ortega, Julio
De-La-Hoz-Franco, Emiro
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/3236
Acceso en línea:
https://hdl.handle.net/11323/3236
https://repositorio.cuc.edu.co/
Palabra clave:
Classification rates
Dimensionality reduction techniques
Intrusion detection systems
Network anomaly detection
Network intrusions
Nonlinear projections
Support vector classifiers
Support vector classifiers ensemble
Tasas de clasificación
Técnicas de reducción de la dimensionalidad
Sistemas de detección de intrusos
Detección de anomalías de red
Intrusiones de red
Proyecciones no lineales
Vector de soporte clasificadores
Conjunto de clasificadores de vectores de apoyo
Rights
openAccess
License
Attribution-NonCommercial-ShareAlike 4.0 International
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dc.title.spa.fl_str_mv Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
dc.title.translated.spa.fl_str_mv Clasificación de anomalías de red por conjuntos de clasificadores de vectores de soporte y técnicas de proyección no lineales
title Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
spellingShingle Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
Classification rates
Dimensionality reduction techniques
Intrusion detection systems
Network anomaly detection
Network intrusions
Nonlinear projections
Support vector classifiers
Support vector classifiers ensemble
Tasas de clasificación
Técnicas de reducción de la dimensionalidad
Sistemas de detección de intrusos
Detección de anomalías de red
Intrusiones de red
Proyecciones no lineales
Vector de soporte clasificadores
Conjunto de clasificadores de vectores de apoyo
title_short Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
title_full Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
title_fullStr Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
title_full_unstemmed Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
title_sort Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
dc.creator.fl_str_mv De La Hoz, Eduardo
Ortiz, Andrés
Ortega, Julio
De-La-Hoz-Franco, Emiro
dc.contributor.author.spa.fl_str_mv De La Hoz, Eduardo
Ortiz, Andrés
Ortega, Julio
De-La-Hoz-Franco, Emiro
dc.subject.spa.fl_str_mv Classification rates
Dimensionality reduction techniques
Intrusion detection systems
Network anomaly detection
Network intrusions
Nonlinear projections
Support vector classifiers
Support vector classifiers ensemble
Tasas de clasificación
Técnicas de reducción de la dimensionalidad
Sistemas de detección de intrusos
Detección de anomalías de red
Intrusiones de red
Proyecciones no lineales
Vector de soporte clasificadores
Conjunto de clasificadores de vectores de apoyo
topic Classification rates
Dimensionality reduction techniques
Intrusion detection systems
Network anomaly detection
Network intrusions
Nonlinear projections
Support vector classifiers
Support vector classifiers ensemble
Tasas de clasificación
Técnicas de reducción de la dimensionalidad
Sistemas de detección de intrusos
Detección de anomalías de red
Intrusiones de red
Proyecciones no lineales
Vector de soporte clasificadores
Conjunto de clasificadores de vectores de apoyo
description Network anomaly detection is currently a challenge due to the number of different attacks and the number of potential attackers. Intrusion detection systems aim to detect misuses or network anomalies in order to block ports or connections, whereas firewalls act according to a predefined set of rules. However, detecting the specific anomaly provides valuable information about the attacker that may be used to further protect the system, or to react accordingly. This way, detecting network intrusions is a current challenge due to growth of the Internet and the number of potential intruders. In this paper we present an intrusion detection technique using an ensemble of support vector classifiers and dimensionality reduction techniques to generate a set of discriminant features. The results obtained using the NSL-KDD dataset outperforms previously obtained classification rates
publishDate 2013
dc.date.issued.none.fl_str_mv 2013-09-11
dc.date.accessioned.none.fl_str_mv 2019-05-07T14:44:22Z
dc.date.available.none.fl_str_mv 2019-05-07T14:44:22Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 0302-9743
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/3236
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0302-9743
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/3236
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv spa
language spa
dc.rights.spa.fl_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
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http://creativecommons.org/licenses/by-nc-sa/4.0/
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eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Universidad De La Costa
institution Corporación Universidad de la Costa
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spelling De La Hoz, EduardoOrtiz, AndrésOrtega, JulioDe-La-Hoz-Franco, Emiro2019-05-07T14:44:22Z2019-05-07T14:44:22Z2013-09-110302-9743https://hdl.handle.net/11323/3236Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Network anomaly detection is currently a challenge due to the number of different attacks and the number of potential attackers. Intrusion detection systems aim to detect misuses or network anomalies in order to block ports or connections, whereas firewalls act according to a predefined set of rules. However, detecting the specific anomaly provides valuable information about the attacker that may be used to further protect the system, or to react accordingly. This way, detecting network intrusions is a current challenge due to growth of the Internet and the number of potential intruders. In this paper we present an intrusion detection technique using an ensemble of support vector classifiers and dimensionality reduction techniques to generate a set of discriminant features. The results obtained using the NSL-KDD dataset outperforms previously obtained classification ratesLa detección de anomalías en la red es actualmente un desafío debido a la cantidad de ataques diferentes y el número de posibles atacantes. Los sistemas de detección de intrusos apuntan a detectar los abusos. o anomalías de la red para bloquear puertos o conexiones, mientras que los firewalls actúan de acuerdo a un conjunto predefinido de reglas. Sin embargo, la detección de la anomalía específica proporciona información valiosa sobre el atacante que puede usarse para proteger aún más el sistema, o para reaccionar en consecuencia. De esta manera, la detección de intrusiones en la red es un desafío actual debido a el crecimiento de Internet y el número de posibles intrusos. En este trabajo presentamos una técnica de detección de intrusos utilizando un conjunto de clasificadores de vectores de soporte y técnicas de reducción de dimensionalidad para generar un conjunto de características discriminantes. Los resultados obtenido utilizando el conjunto de datos NSL-KDD supera las tasas de clasificación obtenidas previamenteDe La Hoz, Eduardo-2be42b28-bb5f-4aad-a7c9-8f41b82fb985-0Ortiz, Andrés-3743e2e5-f13e-4950-8c12-d42d0ab7ccfe-0Ortega, Julio-3b8c20e7-bbcc-4bbd-8ad8-37acc5756525-0De la Hoz, Emiro-will be generated-orcid-0000-0002-4926-7414-600spaUniversidad De La CostaAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Classification ratesDimensionality reduction techniquesIntrusion detection systemsNetwork anomaly detectionNetwork intrusionsNonlinear projectionsSupport vector classifiersSupport vector classifiers ensembleTasas de clasificaciónTécnicas de reducción de la dimensionalidadSistemas de detección de intrusosDetección de anomalías de redIntrusiones de redProyecciones no linealesVector de soporte clasificadoresConjunto de clasificadores de vectores de apoyoNetwork anomaly classification by support vector classifiers ensemble and non-linear projection techniquesClasificación de anomalías de red por conjuntos de clasificadores de vectores de soporte y técnicas de proyección no linealesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALNetwork anomaly classification by support vector classifiers.pdfNetwork anomaly classification by support vector classifiers.pdfapplication/pdf180702https://repositorio.cuc.edu.co/bitstreams/0d5c604f-8806-4124-b8fc-87bcca3c6f89/downloadb24c92fc46e2dccade7442f1afee7faeMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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