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dc.contributor.authorDe la Hoz, Emiro
dc.contributor.authorOrtiz-Garcia, Andres 
dc.contributor.authorOrtega, Julio
dc.contributor.authorDe la Hoz, Eduardo
dc.date.accessioned2013-09-17T08:43:19Z
dc.date.available2013-09-17T08:43:19Z
dc.date.issued2013-09
dc.identifier.urihttp://hdl.handle.net/10630/5726
dc.description.abstractNetwork 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.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectRedes de ordenadores - Medidas de seguridades_ES
dc.subject.othernetwork anomalyes_ES
dc.subject.otherkernel pcaes_ES
dc.subject.otherisomapes_ES
dc.subject.othersupport vector machine ensemblees_ES
dc.titleNetwork Anomaly Classification by Support Vector Classifiers Ensemble and Non-linear Projection Techniqueses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. de Telecomunicaciónes_ES


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