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CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks

  • Cristian Pinzón
  • Álvaro Herrero
  • Juan F. De Paz
  • Emilio Corchado
  • Javier Bajo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

One of the most serious security threats to recently deployed databases has been the SQL Injection attack. This paper presents an agent specialised in the detection of SQL injection attacks. The agent incorporates a Case-Based Reasoning engine which is equipped with a learning and adaptation capacity for the classification of malicious codes. The agent also incorporates advanced algorithms in the reasoning cycle stages. The reuse phase uses an innovative classification model based on a mixture of a neuronal network together with a Support Vector Machine in order to classify the received SQL queries in the most reliable way. Finally, a visualisation neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The Classifier Agent was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented here.

Keywords

SQL Injection Intrusion Detection CBR SVM Neural Networks 

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References

  1. 1.
    Halfond, W.G.J., Viegas, J., Orso, A.: A Classification of SQL-Injection Attacks and Countermeasures. In: Proceedings of the IEEE International Symposium on Secure Software Engineering, Arlington, VA, USA (2006)Google Scholar
  2. 2.
    Bockermann, C., Apel, M., Meier, M.: Learning SQL for Database Intrusion Detection Using Context-Sensitive Modelling (Extended Abstract). In: Flegel, U., Bruschi, D. (eds.) DIMVA 2009. LNCS, vol. 5587, pp. 196–205. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Kemalis, K., Tzouramanis, T.: SQL-IDS: a specification-based approach for SQL-injection detection. In: Proceedings of the 2008 ACM Symposium on Applied Computing (SAC 2008). ACM, New York (2008)Google Scholar
  4. 4.
    Kiani, M., Clark, A., Mohay, G.: Evaluation of Anomaly Based Character Distribution Models in the Detection of SQL Injection Attacks. In: Third International Conference on Availability, Reliability and Security (ARES 2008). IEEE Computer Society, Washington (2008)Google Scholar
  5. 5.
    Bertino, E., Kamra, A., Early, J.: Profiling Database Applications to Detect SQL Injection Attacks. In: Proceedings of the Performance, Computing, and Communications Conference, IPCCC 2007 (2007)Google Scholar
  6. 6.
    Robertson, W., Vigna, G., Kruegel, C., Kemmerer, R.A.: Using Generalization and Characterization Techniques in the Anomaly-Based Detection of Web Attacks. In: 13th Annual Network and Distributed System Security Symposium, NDSS 2006 (2006)Google Scholar
  7. 7.
    García, V.H., Monroy, R., Quintana, M.: Web Attack Detection Using ID3. In: International Federation for Information Processing (2006)Google Scholar
  8. 8.
    Valeur, F., Mutz, D., Vigna, G.: A Learning-Based Approach to the Detection of SQL Attacks. In: Julisch, K., Krügel, C. (eds.) DIMVA 2005. LNCS, vol. 3548, pp. 123–140. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Corchado, J.M., Laza, R.: Constructing deliberative agents with case-based reasoning technology. International Journal of Intelligent Systems 18, 1227–1241 (2003)CrossRefGoogle Scholar
  10. 10.
    Mukkamala, S., Sung, A.H., Abraham, A.: Intrusion detection using an ensemble of intelligent paradigms. Journal of Network and Computer Applications 28(2), 167–182 (2005)CrossRefGoogle Scholar
  11. 11.
    Carrascosa, C., Bajo, J., Julian, V., Corchado, J.M., Botti, V.: Hybrid multi-agent architecture as a real-time problem-solving model. Expert Systems with Applications 34(1), 2–17 (2008)CrossRefGoogle Scholar
  12. 12.
    Fritzke, B.: A Growing Neural Gas Network Learns Topologies. In: Advances in Neural Information Processing Systems, vol. 7. MIT Press, Cambridge (1995)Google Scholar
  13. 13.
    LeCun, Y., Bottou, L., Orr, G.B., Müller, K.R.: Efficient BackProp. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, p. 9. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  14. 14.
    Corchado, E., Fyfe, C.: Connectionist Techniques for the Identification and Suppression of Interfering Underlying Factors. International Journal of Pattern Recognition and Artificial Intelligence 17(8), 1447–1466 (2003)CrossRefGoogle Scholar
  15. 15.
    Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Mining and Knowledge Discovery 8(3), 203–225 (2004)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Herrero, Á., Corchado, E., Sáiz, L., Abraham, A.: DIPKIP: A Connectionist Knowledge Management System to Identify Knowledge Deficits in Practical Cases. Computational Intelligence 26(1), 26–56 (2010)CrossRefGoogle Scholar
  17. 17.
    Damele, B.: SQLMAP0.5 – Automated SQL Injection Tool (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Cristian Pinzón
    • 1
    • 2
  • Álvaro Herrero
    • 3
  • Juan F. De Paz
    • 1
  • Emilio Corchado
    • 1
  • Javier Bajo
    • 1
  1. 1.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain
  2. 2.Universidad Tecnológica de Panamá, A.P: 0819-07289, Panamá, Rep. De Panamá 
  3. 3.Department of Civil EngineeringUniversity of Burgos, SpainBurgosSpain

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