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dc.contributor.authorCamacho Páez, José 
dc.contributor.authorMacía Fernández, Gabriel 
dc.contributor.authorFuentes García, Noemí Marta 
dc.contributor.authorSaccenti, Edoardo
dc.identifier.citationJ. Camacho, G. Maciá-Fernández, N. M. Fuentes-García and E. Saccenti, "Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats," in IEEE Transactions on Information Forensics and Security. doi: 10.1109/TIFS.2019.2894358es_ES
dc.description.abstractThis paper presents a semi-supervised approach for intrusion detection. The method extends the unsupervised Multivariate Statistical Network Monitoring approach based on Principal Component Analysis by introducing a supervised optimization technique to learn the optimum scaling in the input data. It inherits the advantages of the unsupervised strategy, capable of uncovering new threats, with that of supervised strategies, able of learning the pattern of a targeted threat. The supervised learning is based on an extension of the gradient descent method based on Partial Least Squares (PLS). Moreover, we enhance this method by using sparse PLS variants. The practical application of the system is demonstrated on a recently published real case study, showing relevant improvements in detection performance and in the interpretation of the attacks.es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectMultivariate Statistical Network Monitoringes_ES
dc.subjectAnomaly Detectiones_ES
dc.subjectIntrusion Detectiones_ES
dc.subjectSemi-supervised learninges_ES
dc.subjectPartial Least Squares regressiones_ES
dc.subjectPrincipal components analysises_ES
dc.titleSemi-supervised Multivariate Statistical Network Monitoring for Learning Security Threatses_ES

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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España