Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems

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 super...

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Autores:
Mendoza Palechor, Fabio
De la Hoz Manotas, Alexis Kevin
De-La-Hoz-Franco, Emiro
Ariza Colpas, Paola Patricia
Tipo de recurso:
Article of journal
Fecha de publicación:
2015
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/711
Acceso en línea:
https://hdl.handle.net/11323/711
https://repositorio.cuc.edu.co/
Palabra clave:
System intrusion detection (IDS)
Feature selection toolbox (FEAST)
Isometric feature mapping ISOMAP
Support vector machine (SVM)
Principal component analysis (PCA)
Rights
openAccess
License
Atribución – No comercial – Compartir igual
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/711
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
title Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
spellingShingle Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
System intrusion detection (IDS)
Feature selection toolbox (FEAST)
Isometric feature mapping ISOMAP
Support vector machine (SVM)
Principal component analysis (PCA)
title_short Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
title_full Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
title_fullStr Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
title_full_unstemmed Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
title_sort Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
dc.creator.fl_str_mv Mendoza Palechor, Fabio
De la Hoz Manotas, Alexis Kevin
De-La-Hoz-Franco, Emiro
Ariza Colpas, Paola Patricia
dc.contributor.author.spa.fl_str_mv Mendoza Palechor, Fabio
De la Hoz Manotas, Alexis Kevin
De-La-Hoz-Franco, Emiro
Ariza Colpas, Paola Patricia
dc.subject.eng.fl_str_mv System intrusion detection (IDS)
Feature selection toolbox (FEAST)
Isometric feature mapping ISOMAP
Support vector machine (SVM)
Principal component analysis (PCA)
topic System intrusion detection (IDS)
Feature selection toolbox (FEAST)
Isometric feature mapping ISOMAP
Support vector machine (SVM)
Principal component analysis (PCA)
description 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.
publishDate 2015
dc.date.issued.none.fl_str_mv 2015-12-20
dc.date.accessioned.none.fl_str_mv 2018-11-08T20:41:11Z
dc.date.available.none.fl_str_mv 2018-11-08T20:41:11Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/711
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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identifier_str_mv 1992-8645 
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/711
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Garcia, P., Diaz, J., Macia, G. and Vasquez, E., “Anomaly-based network intrusion detection: Techniques, systems and challenges”, in journal Computers & Security, Vol. 28, pp. 18-28, 2009. [2] Xiaonan, S. and Banzhaf, W., “The use of computational intelligence in intrusion detection systems: A review”, in journal Applied Soft Computing, Vol. 10, pp. 1-35, 2010. [3] Symantec. 2015 Internet Security Threat Report [online]. Available: http://www.symantec.com/security_response/pu blications/threatreport.jsp [4] Cisco Systems. Cisco survey evolving security threats [online]. Available: http://www.enterprisetech.com/2015/04/07/cisc o-survey-sees-evolving-security-threats/ [5] Catania, C., Garcia, C., “Reconocimiento de Patrones en el Trafico de Red Basado en Algoritmos Genéticos”, Revista Iberoamericana de Inteligencia Artificial, Vol 12, pp. 65-75, 2008. [6] De la hoz, E., Ortiz, A., Ortega, J., De la hoz, E. 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. [10] Kuang, L., and Zulkernine, M., “An Anomaly Intrusion Detection Method Using the CSIKNN Algorithm”, in Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 921- 926, 2008. [11] W. Hu, Y. Liao, and V. Vemuri. “Robust Support Vector Machines for Anomaly Detection in Computer Security”. In ICMLA, pp. 168–174, 2003. [12] Tajbakhsh, A., Rahmati, M., and Mirzaei, A., “Intrusion detection using fuzzy association rules”. In Applied Soft Computing, Vol. 9(2), pp. 462-469, 2009. [13] Wang, G., Hao, J., Ma, J., and Huang, L., “A new approach to intrusion detection usingArtificial Neural Networks and fuzzy clustering”. In Expert Systems with Applications, Vol. 37(9), pp. 6225-6232, 2010. [14] Microsoft. Selección de Características (Minería de Datos) [online]. Available: https://msdn.microsoft.com/eses/library/ms175382(v=sql.120).aspx [15] Oporto, S., Aquino, I., Chavez, J., Perez, C., Comparación de Cuatro Técnicas de Selección de Características Envolventes usando Neuronales, Arboles de Decisión, Maquinas de Vector de Soporte y Clasificador Bayesiano. [16] Goldberg, D. And Holland, J., “Genetic algorithms and machine learning”, in Machine learning, Vol. 3(2), pp. 95-99, 1998. [17] Liu, H., & Motoda, H, “Feature selection for knowledge discovery and data mining”, in Springer Science & Business Media, Vol. 454, 2012. [18] University of Manchester. A Feature Selection Toolbox for C and Matlab [online]. Available: http://www.cs.man.ac.uk/~gbrown/fstoolbox/. [19] Van Der Maaten, L., Matlab Toolbox for Dimensionality Reduction [online]. Available : http://lvdmaaten.github.io/drtoolbox/ [20] Lohweg, V., and Mönks, U., “Fuzzy-PatternClassifier Based Sensor Fusion for Machine Conditioning”. INTECH Open Access Publisher, 2010. [21] De la Hoz, E., De La Hoz, E., Ortiz, A., Ortega, J., and Prieto, B., “PCA filtering and probabilistic SOM for network intrusion detection”, in Neurocomputing, vol. 164, pp. 71-81 2015. [22] Turk, M. and Pentland, A., “Eigenfaces for Recognition”, in journal of cognitive neuroscience, Vol. 3, pp. 71-86, 2007. [23] Schölkopf, B., Smola, A., and Müller, K., “Nonlinear component analysis as a kernel eigenvalue problema”, in Neural computation, Vol. 10(5), pp. 1299-1319, 1998. [24] Mika, S., Schölkopf, B., Smola, A. J., Müller, K. R., Scholz, M., and Rätsch, G., “Kernel PCA and De-Noising in Feature Spaces”. In NIPS, Vol. 4, No. 5, pp. 7, 1998. [25] Rosipal, R., Girolami, M., and Trejo, L., “Kernel PCA for feature extraction and denoising in nonlinear regression”. Technical Report No. 4, Department of Computing and Information Systems, University of Paisley, 2000. [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.
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spelling Mendoza Palechor, FabioDe la Hoz Manotas, Alexis KevinDe-La-Hoz-Franco, EmiroAriza Colpas, Paola Patricia2018-11-08T20:41:11Z2018-11-08T20:41:11Z2015-12-201992-8645 https://hdl.handle.net/11323/711Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Mendoza Palechor, Fabio-will be generated-orcid-0000-0002-2755-0841-600De la Hoz Manotas, Alexis Kevin-will be generated-orcid-0000-0002-8328-1076-0De la Hoz, Emiro-will be generated-orcid-0000-0002-4926-7414-600Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600engJournal of Theoretical and Applied Information TechnologyAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2System intrusion detection (IDS)Feature selection toolbox (FEAST)Isometric feature mapping ISOMAPSupport vector machine (SVM)Principal component analysis (PCA)Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systemsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Garcia, P., Diaz, J., Macia, G. and Vasquez, E., “Anomaly-based network intrusion detection: Techniques, systems and challenges”, in journal Computers & Security, Vol. 28, pp. 18-28, 2009. [2] Xiaonan, S. and Banzhaf, W., “The use of computational intelligence in intrusion detection systems: A review”, in journal Applied Soft Computing, Vol. 10, pp. 1-35, 2010. [3] Symantec. 2015 Internet Security Threat Report [online]. Available: http://www.symantec.com/security_response/pu blications/threatreport.jsp [4] Cisco Systems. Cisco survey evolving security threats [online]. Available: http://www.enterprisetech.com/2015/04/07/cisc o-survey-sees-evolving-security-threats/ [5] Catania, C., Garcia, C., “Reconocimiento de Patrones en el Trafico de Red Basado en Algoritmos Genéticos”, Revista Iberoamericana de Inteligencia Artificial, Vol 12, pp. 65-75, 2008. [6] De la hoz, E., Ortiz, A., Ortega, J., De la hoz, E. 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. [10] Kuang, L., and Zulkernine, M., “An Anomaly Intrusion Detection Method Using the CSIKNN Algorithm”, in Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 921- 926, 2008. [11] W. Hu, Y. Liao, and V. Vemuri. “Robust Support Vector Machines for Anomaly Detection in Computer Security”. In ICMLA, pp. 168–174, 2003. [12] Tajbakhsh, A., Rahmati, M., and Mirzaei, A., “Intrusion detection using fuzzy association rules”. In Applied Soft Computing, Vol. 9(2), pp. 462-469, 2009. [13] Wang, G., Hao, J., Ma, J., and Huang, L., “A new approach to intrusion detection usingArtificial Neural Networks and fuzzy clustering”. In Expert Systems with Applications, Vol. 37(9), pp. 6225-6232, 2010. [14] Microsoft. Selección de Características (Minería de Datos) [online]. Available: https://msdn.microsoft.com/eses/library/ms175382(v=sql.120).aspx [15] Oporto, S., Aquino, I., Chavez, J., Perez, C., Comparación de Cuatro Técnicas de Selección de Características Envolventes usando Neuronales, Arboles de Decisión, Maquinas de Vector de Soporte y Clasificador Bayesiano. [16] Goldberg, D. And Holland, J., “Genetic algorithms and machine learning”, in Machine learning, Vol. 3(2), pp. 95-99, 1998. [17] Liu, H., & Motoda, H, “Feature selection for knowledge discovery and data mining”, in Springer Science & Business Media, Vol. 454, 2012. [18] University of Manchester. A Feature Selection Toolbox for C and Matlab [online]. Available: http://www.cs.man.ac.uk/~gbrown/fstoolbox/. [19] Van Der Maaten, L., Matlab Toolbox for Dimensionality Reduction [online]. Available : http://lvdmaaten.github.io/drtoolbox/ [20] Lohweg, V., and Mönks, U., “Fuzzy-PatternClassifier Based Sensor Fusion for Machine Conditioning”. INTECH Open Access Publisher, 2010. [21] De la Hoz, E., De La Hoz, E., Ortiz, A., Ortega, J., and Prieto, B., “PCA filtering and probabilistic SOM for network intrusion detection”, in Neurocomputing, vol. 164, pp. 71-81 2015. [22] Turk, M. and Pentland, A., “Eigenfaces for Recognition”, in journal of cognitive neuroscience, Vol. 3, pp. 71-86, 2007. [23] Schölkopf, B., Smola, A., and Müller, K., “Nonlinear component analysis as a kernel eigenvalue problema”, in Neural computation, Vol. 10(5), pp. 1299-1319, 1998. [24] Mika, S., Schölkopf, B., Smola, A. J., Müller, K. R., Scholz, M., and Rätsch, G., “Kernel PCA and De-Noising in Feature Spaces”. In NIPS, Vol. 4, No. 5, pp. 7, 1998. [25] Rosipal, R., Girolami, M., and Trejo, L., “Kernel PCA for feature extraction and denoising in nonlinear regression”. Technical Report No. 4, Department of Computing and Information Systems, University of Paisley, 2000. [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.PublicationORIGINALFeature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdfFeature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdfapplication/pdf590940https://repositorio.cuc.edu.co/bitstreams/42294898-0a8a-4ffa-a8b2-9af1de4becf7/download73a37d36a46700bfe8927fd332811198MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/5e96c0a0-42e6-4b27-8058-1a5b5eda717c/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILFEATURE SELECTION, LEARNING METRICS AND DIMENSION REDUCTION.pdf.jpgFEATURE SELECTION, LEARNING METRICS AND DIMENSION REDUCTION.pdf.jpgimage/jpeg1799https://repositorio.cuc.edu.co/bitstreams/f3013652-053a-4ebd-8c6d-86a80ed5be74/download565c945c4bcafa0de0b546e690180975MD53Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdf.jpgFeature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdf.jpgimage/jpeg68500https://repositorio.cuc.edu.co/bitstreams/47fc952d-617f-4d0b-b535-a54f64540a1d/downloadbd146141db2ec076e70ca0163280d38bMD55TEXTFeature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdf.txtFeature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdf.txttext/plain28588https://repositorio.cuc.edu.co/bitstreams/069cf031-0f0b-4634-b803-49ba0f466337/download83e5c449525a498012e70dd3095ff764MD5611323/711oai:repositorio.cuc.edu.co:11323/7112024-09-17 14:24:05.729open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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