Show simple item record

dc.creatorDe la Hoz Correa, Eduardo Miguel
dc.creatorDe la Hoz, Emiro
dc.creatorOrtiz, Andrés
dc.creatorOrtega, Julio
dc.creatorPrieto, Beatriz
dc.date.accessioned2018-11-14T21:20:38Z
dc.date.available2018-11-14T21:20:38Z
dc.date.issued2015
dc.identifier.issn0925-2312
dc.identifier.urihttp://hdl.handle.net/11323/1011
dc.description.abstractThe growth of the Internet and, consequently, the number of interconnected computers, has exposed significant amounts of information to intruders and attackers. Firewalls aim to detect violations according to a predefined rule-set and usually block potentially dangerous incoming traffic. However, with the evolution of attack techniques, it is more difficult to distinguish anomalies from normal traffic. Different detection approaches have been proposed, including the use of machine learning techniques based on neural models such as Self-Organizing Maps (SOMs). In this paper, we present a classification approach that hybridizes statistical techniques and SOM for network anomaly detection. Thus, while Principal Component Analysis (PCA) and Fisher Discriminant Ratio (FDR) have been considered for feature selection and noise removal, Probabilistic Self-Organizing Maps (PSOM) aim to model the feature space and enable distinguishing between normal and anomalous connections.spa
dc.language.isoengeng
dc.publisherNeurocomputingeng
dc.rightsAtribución – No comercial – Compartir igualeng
dc.sourceNeurocomputing
dc.subjectBayesian SOMeng
dc.subjectIDSeng
dc.subjectPCA filteringeng
dc.subjectProbabilistic SOMeng
dc.subjectSelf-organizing mapseng
dc.titlePCA filtering and probabilistic SOM for network intrusion detectioneng
dc.typeArticleeng
dc.source.urlhttps://www.sciencedirect.com/science/article/abs/pii/S0925231215002982
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record