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...
- 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 |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_6501 |
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 |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
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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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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