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Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
dc.contributor.author | Mendoza Palechor, Fabio | spa |
dc.contributor.author | De la Hoz Manotas, Alexis Kevin | spa |
dc.contributor.author | De-La-Hoz-Franco, Emiro | spa |
dc.contributor.author | Ariza Colpas, Paola Patricia | spa |
dc.date.accessioned | 2018-11-08T20:41:11Z | |
dc.date.available | 2018-11-08T20:41:11Z | |
dc.date.issued | 2015-12-20 | |
dc.identifier.issn | 1992-8645 | spa |
dc.identifier.uri | http://hdl.handle.net/11323/711 | spa |
dc.description.abstract | This research presents an IDS prototype in Matlab that assess network traffic connections contained in the NSL-KDD dataset, comparing feature selection techniques available in FEAST toolbox, refining prior results applying dimension reduction technique ISOMAP. The classification process used a supervised learning technique called Support Vector Machines (SVM). The comparative analysis related to detection rates by attack category are conclusive that MRMR+PCA+SVM (selection, reduction and classification techniques) combined obtained more promising results, just using 5 of 41 available features in the dataset. The results obtained were: 85.42% normal traffic, 80.77% DoS, 90.41% Probe, 91.78% U2R and 83.25% R2L. | spa |
dc.language.iso | eng | |
dc.publisher | Journal of Theoretical and Applied Information Technology | spa |
dc.rights | Atribución – No comercial – Compartir igual | spa |
dc.subject | System intrusion detection (IDS) | eng |
dc.subject | Feature selection toolbox (FEAST) | eng |
dc.subject | Isometric feature mapping ISOMAP | eng |
dc.subject | Support vector machine (SVM) | eng |
dc.subject | Principal component analysis (PCA) | eng |
dc.title | Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems | eng |
dc.type | Artículo de revista | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
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Available: http://www.ll.mit.edu/ideval/data/1998data.html [33] Stolfo, S., Fan, W., Lee, W., Prodromidis, A., and Chan, P., “Costbased modeling for fraud and intrusion detection: Results from the jam project,” discex, vol. 02, pp. 1130, 2000. [34] Tribak, H., “Análisis Estadístico de Distintas Técnicas de Inteligencia Artificial en Detección de Intrusos”. Tesis Doctoral, 2012. [35] Sabnani, S., Computer Security: A machine learning Approach, Technical Report, University of London, 2008. | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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