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dc.contributorEscuela de Ingenierias Industrial e Informaticaes_ES
dc.contributor.authorPérez, Daniel
dc.contributor.authorAlonso, Serafín
dc.contributor.authorMorán, Antonio
dc.contributor.authorPrada Medrano, Miguel Angel
dc.contributor.authorFuertes Martínez, Juan José 
dc.contributor.authorDomínguez, Manuel
dc.contributor.otherIngenieria de Sistemas y Automaticaes_ES
dc.date2019
dc.date.accessioned2019-11-04T18:08:23Z
dc.date.available2019-11-04T18:08:23Z
dc.identifier.citationInternational Conference on Engineering Applications of Neural Networks, 2019es_ES
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10612/11293
dc.descriptionP. 463-475es_ES
dc.description.abstractIntrusion detection is essential for the security of the components of any network. For that reason, several strategies can be used in Intrusion Detection Systems (IDS) to identify the increasing attempts to gain unauthorized access with malicious purposes including those base on machine learning. Anomaly detection has been applied successfully to numerous domains and might help to identify unknown attacks. However, there are existing issues such as high error rates or large dimensionality of data that make its deployment di cult in real-life scenarios. Representation learning allows to estimate new latent features of data in a low-dimensionality space. In this work, anomaly detection is performed using a previous feature learning stage in order to compare these methods for the detection of intrusions in network tra c. For that purpose, four di erent anomaly detection algorithms are applied to recent network datasets using two di erent feature learning methods such as principal component analysis and autoencoders. Several evaluation metrics such as accuracy, F1 score or ROC curves are used for comparing their performance. The experimental results show an improvement for two of the anomaly detection methods using autoencoder and no signi cant variations for the linear feature transformationes_ES
dc.languageenges_ES
dc.publisherSpringeres_ES
dc.subjectIngeniería de sistemases_ES
dc.subject.otherAnomaly detectiones_ES
dc.subject.otherFeature representationes_ES
dc.subject.otherNetwork intrusion detectiones_ES
dc.titleComparison of Network Intrusion Detection Performance Using Feature Representationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1007/978-3-030-20257-6_40
dc.description.peerreviewedSIes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.essn1865-0937
dc.journal.titleInternational Conference on Engineering Applications of Neural Networkses_ES
dc.page.initial463es_ES
dc.page.final475es_ES
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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