Application of feast (Feature Selection Toolbox) in ids (Intrusion detection Systems)
Abstract
Security in computer networks has become a critical point for many organizations, but keeping data integrity demands time and large economic investments, in consequence there has been several solution approaches between hardware and software but sometimes these has become inefficient for attacks detection. This paper presents research results obtained implementing algorithms from FEAST, a Matlab Toolbox with the purpose of selecting the method with better precision results for different attacks detection using the least number of features. The Data Set NSL-KDD was taken as reference. The Relief method obtained the best precision levels for attack detection: 86.20%(NORMAL), 85.71% (DOS), 88.42% (PROBE), 93.11%(U2R), 90.07(R2L), which makes it a promising technique for features selection in data network intrusions.
Referencias:
[1] M. Crosbie and G. Spafford., “Applying genetic programming to intrusion detection. “ in AAAI Fall Symposium on Genetic Pro- gramming, 1995. [2] R. Gong, M. Zulkernine, and P. Abolmaesmumi, “A software implementation of a genetic algorithm based approach to network intrusion detection.,” in Sixth Internatio- nal Conference on Software Engineering, Artificial Intelligence, Networking and Para- llel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNDP/SWAN’05), vol. 0, pp. 246– 253, 2005. [3] W. Li, “A genetic approach to network intrusion detection,” tech. rep., SANS Institute, 2003. [4] C. Sinclair, P. Lyn, and S. Matzer, “An application of machine learning to network intrusion detection.,” in 15th Annual Computer Security Applications Conference, 1999. [5] Herrera, D., Carvajal, Helber., IMPLEMENTACIÓN DE UNA RED NEURONAL PARA LA DETECCIÓN DE INTRUSIONES EN UNA RED TCP/IP, Revista Ingenierías USBMed, paginas 45-48, 2010. [6] Kayacık, G., Zincir, N., Heywood, M., Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets , Dalhousie University, Faculty of Computer Science [7] P. Ananthi y P. Balasubramanie, «A Fuzzy Neural Network And Multiple Kernel Fuzzy CMeans Algorithm For Secured Intrusion Detection System,» Journal of Theoretical and Applied Information Technology (JATIT), pp. 206-217. [8] A. Falke, V. Fulsoundar, R. Pawase, S. Wale y S. Ghule, «Network Intrusion Detection System using Fuzzy Logic,» nternational Journal Of Scientific Research And Education, pp. 626- 635. [9] Castillo, R., Deteccion de Intrusos Mediante Tecnicas de Mineria de Datos, Departamento de Sistemas e Informatica, Universidad Autonoma de Colombia. [10] Lorenzo, I., Macia, F., Mora, F., Gil, J., Marcos, J., Modelo Eficiente y Escalable para la Deteccion de Intrusos en Red, Departamento de Tecnologia y Computacion, Universidad de Alicante. [11] Catania, C., Garcia, C., 2008, Reconocimiento de Patrones en el Trafico de Red Basado en Algoritmos Geneticos, Revista Iberoamericana de Inteligencia Artificial, Vol 12, 65-75. [12] Xiaoqing, G., Hebin, G., Luyi, C., Network Intrusion Detection Method Based on Agent and SVM, Beijing Vocational College of Electronic Science Beijing 100026,P.R. China. [13] Kuang, L., Zulkernine, M., An Anomaly Intrusion Detection Method Using the CSIKNN Algorithm School of Computing Queen’s University Kingston, Canada. [14] W. Hu, Y. Liao, and V. Vemuri. Robust Support Vector Machines for Anomaly Detection in Computer Security. Proc. International Conference on Machine Learning and Applications, pages 23–24, 2003. [15] Oporto, Sl., Aquino, I., Chavez, J., Perez, C., Comparacion de Cuatro Tecnicas de Selección de Caracteristicas Envolventes usando Neuronales, Arboles de Decisión, Maquinas de Vector de Soporte y Clasificador Bayesiano. [16] D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning. AddisonWesley. [17] H. Liu and H. Motorola. Feature Selection for Knowledge Discovery and Data Mining. Boston: Kluwer Academy, (1998). [18] http://www.cs.man.ac.uk/~gbrown/fstoolbox/ (07/11/2013) [19] S. J. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. K. Chan, “Costbased modeling for fraud and intrusion detection: Results from the jam project,” discex, vol. 02, p. 1130, 2000. [20] Tribak, H., Febrero 2012, Análisis Estadístico de Distintas Técnicas de Inteligencia Artificial en Detección de Intrusos. Tesis Doctoral
[1] M. Crosbie and G. Spafford., “Applying genetic programming to intrusion detection. “ in AAAI Fall Symposium on Genetic Pro- gramming, 1995. [2] R. Gong, M. Zulkernine, and P. Abolmaesmumi, “A software implementation of a genetic algorithm based approach to network intrusion detection.,” in Sixth Internatio- nal Conference on Software Engineering, Artificial Intelligence, Networking and Para- llel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNDP/SWAN’05), vol. 0, pp. 246– 253, 2005. [3] W. Li, “A genetic approach to network intrusion detection,” tech. rep., SANS Institute, 2003. [4] C. Sinclair, P. Lyn, and S. Matzer, “An application of machine learning to network intrusion detection.,” in 15th Annual Computer Security Applications Conference, 1999. [5] Herrera, D., Carvajal, Helber., IMPLEMENTACIÓN DE UNA RED NEURONAL PARA LA DETECCIÓN DE INTRUSIONES EN UNA RED TCP/IP, Revista Ingenierías USBMed, paginas 45-48, 2010. [6] Kayacık, G., Zincir, N., Heywood, M., Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets , Dalhousie University, Faculty of Computer Science [7] P. Ananthi y P. Balasubramanie, «A Fuzzy Neural Network And Multiple Kernel Fuzzy CMeans Algorithm For Secured Intrusion Detection System,» Journal of Theoretical and Applied Information Technology (JATIT), pp. 206-217. [8] A. Falke, V. Fulsoundar, R. Pawase, S. Wale y S. Ghule, «Network Intrusion Detection System using Fuzzy Logic,» nternational Journal Of Scientific Research And Education, pp. 626- 635. [9] Castillo, R., Deteccion de Intrusos Mediante Tecnicas de Mineria de Datos, Departamento de Sistemas e Informatica, Universidad Autonoma de Colombia. [10] Lorenzo, I., Macia, F., Mora, F., Gil, J., Marcos, J., Modelo Eficiente y Escalable para la Deteccion de Intrusos en Red, Departamento de Tecnologia y Computacion, Universidad de Alicante. [11] Catania, C., Garcia, C., 2008, Reconocimiento de Patrones en el Trafico de Red Basado en Algoritmos Geneticos, Revista Iberoamericana de Inteligencia Artificial, Vol 12, 65-75. [12] Xiaoqing, G., Hebin, G., Luyi, C., Network Intrusion Detection Method Based on Agent and SVM, Beijing Vocational College of Electronic Science Beijing 100026,P.R. China. [13] Kuang, L., Zulkernine, M., An Anomaly Intrusion Detection Method Using the CSIKNN Algorithm School of Computing Queen’s University Kingston, Canada. [14] W. Hu, Y. Liao, and V. Vemuri. Robust Support Vector Machines for Anomaly Detection in Computer Security. Proc. International Conference on Machine Learning and Applications, pages 23–24, 2003. [15] Oporto, Sl., Aquino, I., Chavez, J., Perez, C., Comparacion de Cuatro Tecnicas de Selección de Caracteristicas Envolventes usando Neuronales, Arboles de Decisión, Maquinas de Vector de Soporte y Clasificador Bayesiano. [16] D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning. AddisonWesley. [17] H. Liu and H. Motorola. Feature Selection for Knowledge Discovery and Data Mining. Boston: Kluwer Academy, (1998). [18] http://www.cs.man.ac.uk/~gbrown/fstoolbox/ (07/11/2013) [19] S. J. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. K. Chan, “Costbased modeling for fraud and intrusion detection: Results from the jam project,” discex, vol. 02, p. 1130, 2000. [20] Tribak, H., Febrero 2012, Análisis Estadístico de Distintas Técnicas de Inteligencia Artificial en Detección de Intrusos. Tesis Doctoral
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