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An Attack Detection Mechanism Based on a Distributed Hierarchical Multi-agent Architecture for Protecting Databases

  • Cristian Pinzón
  • Yanira de Paz
  • Rosa Cano
  • Manuel P. Rubio
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 55)

Abstract

This paper presents an innovative approach to detect and classify SQL injection attacks. The existing approaches are centralized while this proposal is based on a distributed hierarchical architecture to provide a robust and dynamic strategy. The strategy for the classification and detection of SQL injection attacks uses a combination based on detection by anomalies and misuses. The detection by anomaly uses a case-based reasoning mechanism incorporating a mixture of neural networks. The approach has been tested and the results are presented in this paper.

Keywords

SQL injection Security database IDS Multi-agent case-based reasoning 

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References

  1. 1.
    Halfond, W., Orso, A.: AMNESIA: Analysis and Monitoring for Neutralizing SQL-injection Attacks. In: 20th IEEE/ACM international Conference on Automated software engineering, pp. 174–183. ACM, New York (2005)CrossRefGoogle Scholar
  2. 2.
    Kosuga, Y., Kono, K., Hanaoka, M., Hishiyama, M., Takahama, Y.: Sania: Syntactic and Semantic Analysis for Automated Testing against SQL Injection. In: 23rd Annual Computer Security Applications Conference, pp. 107–117. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  3. 3.
    Valeur, F., Mutz, D., Vigna, G.: A Learning-Based Approach to the Detection of SQL Attacks. In: Conference on Detection of Intrusions and Malware and Vulnerability Assessment, Vienna, pp. 123–140 (2005)Google Scholar
  4. 4.
    Rietta, F.: Application layer intrusion detection for SQL injection. In: 44th annual Southeast regional conference, pp. 531–536. ACM, New York (2006)CrossRefGoogle Scholar
  5. 5.
    Abraham, A., Jain, R., Thomas, J., Han, S.Y.: D-SCIDS: distributed soft computing intrusion detection system. Journal of Network and Computer Applications 30, 81–98 (2007)CrossRefGoogle Scholar
  6. 6.
    Woolridge, M., Wooldridge, M.J.: Introduction to Multiagent Systems. John Wiley & Sons, Inc., New York (2002)Google Scholar
  7. 7.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7, 39–59 (1994)Google Scholar
  8. 8.
    Laza, R., Pavon, R., Corchado, J.M.: A Reasoning Model for CBR_BDI Agents Using an Adaptable Fuzzy Inference System. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, J.-L. (eds.) CAEPIA/TTIA 2003. LNCS, vol. 3040, pp. 96–106. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Anley, C.: Advanced SQL Injection. In: SQL Server Applications (2002), http://www.nextgenss.com/papers/advancedsqlinjection.pdf
  10. 10.
    Christensen, A.S., Moller, A., Schwartzbach, M.I.: Precise Analysis of String Expressions. In: Cousot, R. (ed.) SAS 2003. LNCS, vol. 2694, pp. 1–18. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Su, Z., Wassermann, G.: The essence of command injection attacks in web applications. In: 33rd Annual Symposium on Principles of Programming Languages, pp. 372–382. ACM Press, New York (2006)Google Scholar
  12. 12.
    Huang, Y., Huang, S., Lin, T., Tsai, C.: Web application security assessment by fault injection and behavior monitoring. In: 12th international conference on World Wide Web, pp. 148–159. ACM, New York (2003)Google Scholar
  13. 13.
    Skaruz, J., Seredynski, F.: Recurrent neural networks towards detection of SQL attacks. In: 21th International Parallel and Distributed Processing Symposium, pp. 1–8. IEEE International, Los Alamitos (2007)CrossRefGoogle Scholar
  14. 14.
    Ramasubramanian, P., Kannan, A.: Quickprop Neural Network Ensemble Forecasting a Database Intrusion Prediction System. In: 7th International Conference Artificial on Intelligence and Soft Computing, Neural Information Processing, vol. 5, pp. 847–852 (2004)Google Scholar
  15. 15.
    Corchado, J.M., Bajo, J., Abraham, A.: GerAmi: Improving Healthcare Delivery in Geriatric Residences. Intelligent Systems 23, 19–25 (2008)CrossRefGoogle Scholar
  16. 16.
    Corchado, J.M., Bajo, J., de Paz, Y., Tapia, D.: Intelligent Environment for Monitoring Alzheimer Patients, Agent Technology for Health Care. Decision Support Systems 44(2), 382–396 (2008)CrossRefGoogle Scholar
  17. 17.
    Corchado, J.M., Gonzalez-Bedia, M., De Paz, Y., Bajo, J., De Paz, J.F.: Replanning mechanism for deliberative agents in dynamic changing environments. Computational Intelligence 24(2), 77–107 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cristian Pinzón
    • 1
  • Yanira de Paz
    • 2
  • Rosa Cano
    • 3
  • Manuel P. Rubio
    • 4
  1. 1.Universidad Tecnológica de PanamáPanama
  2. 2.Universidad Europea de MadridVillaviciosa de OdónSpain
  3. 3.Instituto Tecnológico de ColimaMexico
  4. 4.Escuela Politécnica Superior de ZamoraZamoraSpain

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