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An Unsupervised Cooperative Pattern Recognition Model to Identify Anomalous Massive SNMP Data Sending

  • Álvaro Herrero
  • Emilio Corchado
  • José Manuel Sáiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3610)

Abstract

In this paper, we review a visual approach and propose it for analysing computer-network activity, which is based on the use of unsupervised connectionist neural network models and does not rely on any previous knowledge of the data being analysed. The presented Intrusion Detection System (IDS) is used as a method to investigate the traffic which travels along the analysed network, detecting SNMP (Simple Network Management Protocol) anomalous traffic patterns. In this paper we have focused our attention on the study of anomalous situations generated by a MIB (Management Information Base) information transfer.

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References

  1. 1.
    Planquart, J.-P.: Application of Neural Networks to Intrusion Detection. Information Security Reading Room - SANS (SysAdmin, Audit, Network, Security) Institute (2002)Google Scholar
  2. 2.
    Hätönen, K., Höglund, A., Sorvari, A.: A Computer Host-Based User Anomaly Detection System Using the Self-Organizing Map. In: International Joint Conference of Neural Networks (2000)Google Scholar
  3. 3.
    Ghosh, A., Schwartzbard, A., Schatz, A.: Learning Program Behavior Profiles for Intrusion Detection. In: Workshop on Intrusion Detection and Network Monitoring (1999)Google Scholar
  4. 4.
    Debar, H., Becker, M., Siboni, D.: A Neural Network Component for an Intrusion Detection System. In: IEEE Symposium on Research in Computer Security and Privacy (1992)Google Scholar
  5. 5.
    Corchado, E., Herrero, A., Baruque, B., Saiz, J.M.: Intrusion Detection System Based on a Cooperative Topology Preserving Method. In: International Conference on Adaptive and Natural Computing Algorithms. Springer Computer Science. Springer/Wien, NewYork (2005)Google Scholar
  6. 6.
    Friedman, J., Tukey, J.: A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers (23), 881–890 (1974)Google Scholar
  7. 7.
    Hyvärinen, A.: Complexity Pursuit: Separating Interesting Components from Time Series. Neural Computation 13, 883–898 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: ESANN (2002)Google Scholar
  10. 10.
    Corchado, E., Han, Y., Fyfe, C.: Structuring Global Responses of Local Filters Using Lateral Connections. JETAI 15(4), 473–487 (2003)zbMATHCrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. Advances in Neural Information Processing Systems 10, 350 (1998)Google Scholar
  13. 13.
    Fyfe, C.: A Neural Network for PCA and Beyond. Neural Processing Letters 6 (1996)Google Scholar
  14. 14.
    Case, J., Fedor, M.S., Schoffstall, M.L., Davin, C.: Simple Network Management (SNMP). RFC-1157 (1990)Google Scholar
  15. 15.
    Postel, J.: IAB Official Protocol Standards. RFC-1100 (1989)Google Scholar
  16. 16.
    Oja, E.: Neural Networks, Principal Components and Subspaces. International Journal of Neural Systems 1, 61–68 (1989)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure, 1st edn. Morgan Kaufmann Publishers, San Francisco (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Álvaro Herrero
    • 1
  • Emilio Corchado
    • 1
  • José Manuel Sáiz
    • 1
  1. 1.Department of Civil EngineeringUniversity of BurgosSpain

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