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dc.contributor.authorMendoza Palechor, Fabiospa
dc.contributor.authorDe la Hoz Manotas, Alexis Kevinspa
dc.contributor.authorDe-La-Hoz-Franco, Emirospa
dc.contributor.authorAriza Colpas, Paola Patriciaspa
dc.date.accessioned2018-11-08T20:41:11Z
dc.date.available2018-11-08T20:41:11Z
dc.date.issued2015-12-20
dc.identifier.issn1992-8645 spa
dc.identifier.urihttp://hdl.handle.net/11323/711spa
dc.description.abstractThis 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.isoeng
dc.publisherJournal of Theoretical and Applied Information Technologyspa
dc.rightsAtribución – No comercial – Compartir igualspa
dc.subjectSystem intrusion detection (IDS)eng
dc.subjectFeature selection toolbox (FEAST)eng
dc.subjectIsometric feature mapping ISOMAPeng
dc.subjectSupport vector machine (SVM)eng
dc.subjectPrincipal component analysis (PCA)eng
dc.titleFeature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systemseng
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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And Mendoza, F., “Implementation of an Intrusion Detection System Based on Self Organizing Map”, in Journal of Theoretical and Applied Information Technology, Vol. 71, pp. 324-334, 2015. [7] Mendoza, F., De la hoz, E. And De la hoz, A., “Application of Feast (Feature Selection Toolbox) in IDS (Intrusion Detection Systems)”, in Journal of Theoretical and Applied Information Technology, Vol. 70, pp. 579-585, 2014. [8] Lorenzo, I., Macia, F., Mora, F., Gil, J., and Marcos, J., “Modelo Eficiente y Escalable para la Deteccion de Intrusos en Red”, in XXIV Simposium Nacional de la Unión Científica Internacional de Radio (URSI'09), 2009. [9] Xiaoqing, G., Hebin, G., and Luyi, C., “Network Intrusion Detection Method Based on Agent and SVM”, in Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on, pp. 399 – 402, 2010. 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[26] Xiao, X., and Tao, C., “ISOMAP AlgorithmBased Feature Extraction for Electromechanical Equipment Fault Prediction”, in Image and Signal Processing, 2009. CISP '09. 2nd International Congress on, pp. 1-4, 2009. [27] Burges, C., Schölkopf, B. And Smola, A., “Advances in kernel methods: Support vector machines”. Cambridge, MA: MIT Press, 1999. [28] Burges, C., “A tutorial on support vector machines for pattern recognition”. Data Mining and Knowledge Discovery, vol. 2, no. 2, 1998. [29] Vapnik, V., “The nature of statistical learning theory”. New York: Springer-Verlag, 1995. [30] Betancourt, G., Las Maquinas de Soporte Vectorial (SVMs), Universidad Tecnológica de Pereira, 2005. [31] University of California. The UCI KDD Archive [online]. Available: http://kdd.ics.uci.edu/databases/kddcup99/kddc up99.html. [32] MIT Lincoln Laboratory. 1998 DARPA Intrusion Detection Evaluation Data Set. [online]. 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
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