Mathematical model for a temporal-bounded classifier in security environments

Security is a major concern when web applications are implemented. This has led to the proposal of a variety of specifications and approaches to provide the necessary security for these environments. SQL injection attacks on web applications have become one of the most important information security concerns over the past few years. The purpose of this article is to present an adaptive and intelligent mechanism that can handle SQL injection attacks taking into account a controlled time response. Our approach is based on a soft real-time classifier agent that incorporates a mixture of experts based on soft computing to choose a specific classification technique depending on the attack and the time available to solve the classification. A case study to evaluate the effectiveness of the approach and the preliminary results obtained with an initial prototype are also presented.Security is a major concern when web applications are implemented. This has led to the proposal of a variety of specifications and approaches to provide the necessary security for these environments. SQL injection attacks on web applications have become one of the most important information security concerns over the past few years. The purpose of this article is to present an adaptive and intelligent mechanism that can handle SQL injection attacks taking into account a controlled time response. Our approach is based on a soft real-time classifier agent that incorporates a mixture of experts based on soft computing to choose a specific classification technique depending on the attack and the time available to solve the classification. A case study to evaluate the effectiveness of the approach and the preliminary results obtained with an initial prototype are also presented

Mathematical model for a temporal-bounded classifier in security environments

Security is a major concern when web applications are implemented. This has led to the proposal of a variety of specifications and approaches to provide the necessary security for these environments. SQL injection attacks on web applications have become one of the most important information security concerns over the past few years. The purpose of this article is to present an adaptive and intelligent mechanism that can handle SQL injection attacks taking into account a controlled time response. Our approach is based on a soft real-time classifier agent that incorporates a mixture of experts based on soft computing to choose a specific classification technique depending on the attack and the time available to solve the classification. A case study to evaluate the effectiveness of the approach and the preliminary results obtained with an initial prototype are also presented. © 2011 The Author. Published by Oxford University Press. All rights reserved.MICINN TIN 2009-13839-C03 and CSD2007-00022 projects; Professional Excellence Program 2006-2010 IFARHU-SENACYT-Panama.Paz, JFD.; Navarro Llácer, M.; Pinzón, CI.; Julian Inglada, VJ.; Tapia, DI.; Bajo, J. (2012). Mathematical model for a temporal-bounded classifier in security environments. Logic Journal of the IGPL. 20(4):712-721. https://doi.org/10.1093/jigpal/jzr015S712721204CORCHADO, E., & FYFE, C. (2003). CONNECTIONIST TECHNIQUES FOR THE IDENTIFICATION AND SUPPRESSION OF INTERFERING UNDERLYING FACTORS. International Journal of Pattern Recognition and Artificial Intelligence, 17(08), 1447-1466. doi:10.1142/s0218001403002915Garvey, A. J., & Lesser, V. R. (1993). Design-to-time real-time scheduling. IEEE Transactions on Systems, Man, and Cybernetics, 23(6), 1491-1502. doi:10.1109/21.257749Herrero, Á., Corchado, E., Sáiz, L., & Abraham, A. (2010). DIPKIP: A CONNECTIONIST KNOWLEDGE MANAGEMENT SYSTEM TO IDENTIFY KNOWLEDGE DEFICITS IN PRACTICAL CASES. Computational Intelligence, 26(1), 26-56. doi:10.1111/j.1467-8640.2009.00351.xIvanciuc, O. (2007). Applications of Support Vector Machines in Chemistry. Reviews in Computational Chemistry, 291-400. doi:10.1002/9780470116449.ch6Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive Mixtures of Local Experts. Neural Computation, 3(1), 79-87. doi:10.1162/neco.1991.3.1.79Julian, V., & Botti, V. (2004). Developing real-time multi-agent systems. Integrated Computer-Aided Engineering, 11(2), 135-149. doi:10.3233/ica-2004-11204Navarro, M., Heras, S., Julián, V., & Botti, V. (2011). Incorporating temporal-bounded CBR techniques in real-time agents. Expert Systems with Applications, 38(3), 2783-2796. doi:10.1016/j.eswa.2010.08.070Sedano, J., Curiel, L., Corchado, E., de la Cal, E., & Villar, J. R. (2010). A soft computing method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering, 17(2), 103-115. doi:10.3233/ica-2010-0337SUBASI, A. (2007). EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications, 32(4), 1084-1093. doi:10.1016/j.eswa.2006.02.00