Intrusion Detection at Packet Level by Unsupervised Architectures

  • Álvaro Herrero
  • Emilio Corchado
  • Paolo Gastaldo
  • Davide Leoncini
  • Francesco Picasso
  • Rodolfo Zunino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)

Abstract

Intrusion Detection Systems (IDS’s) monitor the traffic in computer networks for detecting suspect activities. Connectionist techniques can support the development of IDS’s by modeling ‘normal’ traffic. This paper presents the application of some unsupervised neural methods to a packet dataset for the first time. This work considers three unsupervised neural methods, namely, Vector Quantization (VQ), Self-Organizing Maps (SOM) and Auto-Associative Back-Propagation (AABP) networks. The former paradigm proves quite powerful in supporting the basic space-spanning mechanism to sift normal traffic from anomalous traffic. The SOM attains quite acceptable results in dealing with some anomalies while it fails in dealing with some others. The AABP model effectively drives a nonlinear compression paradigm and eventually yields a compact visualization of the network traffic progression.

Keywords

Intrusion Detection System Network Security Vector Quantization Self-Organizing Map Auto Associative Back Propagation 

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References

  1. 1.
    Laskov, P., Dussel, P., Schafer, C., Rieck, K.: Learning Intrusion Detection: Supervised or Unsupervised? In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 50–57. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Liao, Y., Vemuri, V.R.: Use of K-nearest Neighbor Classifier for Intrusion Detection. Comput. Security 21(5), 439–448 (2002)CrossRefGoogle Scholar
  3. 3.
    Sarasamma, S.T., Qiuming, A.Z., Huff, J.: Hierarchical Kohonen Net for Anomaly Detection in Network Security. IEEE Trans. on SMC – part B 35(2) (2005)Google Scholar
  4. 4.
    Zanero, S.: Analyzing TCP Traffic Patterns Using Self Organizing Maps. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 83–90. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Zheng, J., Hu, M.: An Anomaly Intrusion Detection System Based on Vector Quantization. ICIE Trans. on Inf. & Syst. E89-D(1) (2006)Google Scholar
  6. 6.
    Ridella, S., Rovetta, S., Zunino, R.: Plastic Algorithm for Adaptive Vector Quantization. Neural Computing & Applications 7, 37–51 (1998)MATHCrossRefGoogle Scholar
  7. 7.
    Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  8. 8.
    Kramer, M.A.: Nonlinear Principal Component Analysis using Autoassociative Neural Networks. AIChE Journal 37(2) (1991)Google Scholar
  9. 9.
    Cisco Secure Consulting: Vulnerability Statistics Report (2000)Google Scholar
  10. 10.
    Corchado, E., Herrero, A., Saiz, J.M.: Detecting Compounded Anomalous SNMP Situations using Unsupervised Pattern Recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 905–910. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Corchado, E., Herrero, A., Saiz, J.M.: Testing CAB-IDS through Mutations: on the Identification of Network Scans. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4252, pp. 433–441. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Elkan, M.: Results of the KDD 1999 Classifier Learning Contest (1999), online from: http://www-cse.ucsd.edu/users/elkan/clresults.html
  13. 13.
    Lippmann, R., Haines, J.W., Fried, D.J., Korba, J., Das, K.: Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation. In: Debar, H., Mé, L., Wu, S.F. (eds.) RAID 2000. LNCS, vol. 1907, pp. 162–182. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Sabhnani, M., Serpen, G.: Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context. In: Proc. MLMTA 2003, pp. 623–630 (2003)Google Scholar
  15. 15.
    Lee, W., Xiang, D.: Information-Theoretic Measures for Anomaly Detection. In: Proc. 2001 IEEE Symp. on Security and Privacy, pp. 130–143 (2001)Google Scholar
  16. 16.
    Lee, W., Stolfo, S.J., Mok, K.W.: Mining in a Data-Flow Environment: Experience in Network Intrusion Detection. In: KDD 1999. Proc. 5th ACM International Conference on Knowledge Discovery and Data Mining, pp. 114–124 (1999)Google Scholar
  17. 17.
    Lee, W., Stolfo, S.J., Mok, K.W.: Adaptive Intrusion Detection: A Data Mining Approach. Artificial Intelligence Review 14(6), 533–567 (2000)MATHCrossRefGoogle Scholar
  18. 18.
    Martinetz, T., Berkovich, S.G., Schulten, K.J.: Neural Gas Network for Vector Quantization and its Application to Time-Series Prediction. IEEE TNN 4(4), 558–569 (1993)Google Scholar
  19. 19.
    Kohonen, T., Lehtio, P., Rovamo, J., Hyvarinen, J., Bry, K., Vainio, L.: Principle of Neural Associative Memory. Neuroscience 2(6), 1065–1076 (1977)CrossRefGoogle Scholar
  20. 20.
    Kiviluoto, K.: Topology Preservation in Self-Organizing Maps. In: IEEE International Conference on Neural Networks, vol. 1, pp. 294–299 (1996)Google Scholar
  21. 21.
    Kohonen, T.: Self-Organizing Maps. Springer Series In Information Sciences, vol. 30. Springer, New York (1997)MATHGoogle Scholar
  22. 22.
    Pearson, K.: On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine 2(6), 559–572 (1901)Google Scholar
  23. 23.
    Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing. MIT Press, Cambridge, MA (1986)Google Scholar
  24. 24.
    Widrow, W., Lehr, M.A.: 30 Years of Adaptive Neural Networks: Perceptron, Madaline and Back Propagation. Proc. IEEE 78(9), 1415–1442 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Álvaro Herrero
    • 1
  • Emilio Corchado
    • 1
  • Paolo Gastaldo
    • 2
  • Davide Leoncini
    • 2
  • Francesco Picasso
    • 2
  • Rodolfo Zunino
    • 2
  1. 1.Department of Civil Engineering, University of Burgos, C/ Francisco de Vitoria s/n, 09006 BurgosSpain
  2. 2.Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145 GenoaItaly

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