Unsupervised Anomaly Detection Based n an Evolutionary Artificial Immune Network

  • Liu Fang
  • Lin Le-Ping
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3449)


To solve the problem of unsupervised anomaly detection, an unsupervised anomaly-detecting algorithm based on an evolutionary artificial immune network is proposed in this paper. An evolutionary artificial immune network is “evolved” by using unlabeled training sample data to represent the distribution of the original input data set. Then a traditional hierarchical agglomerative clustering method is employed to perform clustering analysis within the algorithm. It is shown that the algorithm is feasible and effective with simulations over the 1999 KDD CUP dataset.


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  1. 1.
    Denning, D.E.: An intrusion detection model. IEEE Transactions on Software Engineering SE-13, 222–232 (1987)CrossRefGoogle Scholar
  2. 2.
    Eskin, E.: Anomaly detection over noisy data using learned probability distribution. In: Proceedings of the International Conference on Machine Learning (2000)Google Scholar
  3. 3.
    Eskin, E., Stolfo, S., et al.: A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. Data Mining for Security Applications. Kluwer, Dordrecht (2002)Google Scholar
  4. 4.
    Portnoy, L.: Intrusion Detection with Unlabeled Data using Clustering. Undergraduate Thesis, Columbia University (December 2000)Google Scholar
  5. 5.
    Luo, M., Wang, L.-n., Zhang, H.-g.: An Unsupervised Clustering-Based Intrusion Detection Method. Acta Electronica Sinica 30(11), 1713–1716 (2003)Google Scholar
  6. 6.
    Prerau, M.J., Eskin, E.: Unsupervised Anomaly Detection Using an Optimized K-Nearest Neighbors Algorithm. Undergraduate Thesis, Columbia University (December 2000)Google Scholar
  7. 7.
    de Castro, L.N., Von Zuben, F.J.: An Evolutionary Immune Network for Data Clustering. In: Proc. of the IEEE SBRN, pp. 84–89 (November 2000)Google Scholar
  8. 8.
    de Castro, L.N., Timmis, J.: Hierarchy and Convergence of Immune Networks: Basic Ideas and Preliminary Results. In: 1st ICARIS (2002)Google Scholar
  9. 9.
  10. 10.
    Licheng, J., Haifeng, D.: An Artificial Immune System: Progress and Prospect. Acta Electronica Sinica 31(10), 1540–1549 (2003)Google Scholar
  11. 11.
    Burnett, F.M.: The Clonal Selection Theory of Immunity. Vanderbilt University Press, Nashville (1959)Google Scholar
  12. 12.
    Jerne, N.K.: Towards a Network Theory of the Immune System. Ann. Immunol (Inst. Pasteur) 125C, 373–389 (1974)Google Scholar
  13. 13.
    Results of the KDD 1999 Classifier Learning Contest,

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Liu Fang
    • 1
  • Lin Le-Ping
    • 1
  1. 1.School of Computer Science and EngineeringXidian UniversityXi’anChina

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