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A New Artificial Immune System for the Detection of Abnormal Behaviour

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 149))

Summary

We propose in this paper a new Artificial Immune System (AIS) named NK system, for the detection of abnormal behaviour with an unsupervised approach. Its originality resides in the unsupervised detection based on the mechanism of NK cell (Natural Killer cell) contrary to the existing AIS that use supervised approaches based on the mechanisms of the T and B cells. The NK cells develop the capacity to recognize the molecules of self-MHC through a unique class of receptors that can inhibit or activate its natural mechanism of the antigens elimination. In this paper, the NK system is applied to the detection of fraud in mobile phone. The experimental results are very satisfactory instead of the very weak proportion of the fraudulent operations in our sample.

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References

  1. Aickelin, U., Cayzer, S.: The danger theory and its application to artificial immune systems. In: The proceedings of 1st International Conference on Artificial Immune Systems (ICARIS), University of Kent at Canterbury, UK, pp. 141–148 (2002)

    Google Scholar 

  2. Bandt, C., Prompe, B.: Permutation entropy - a natural complexity measure for time series. Phys. Rev, Lett. 88, 174102 (2002)

    Article  Google Scholar 

  3. Burnet, F.: The clonal selection theory of acquired immunity. Vanderbilt University Press, Nashville (1959)

    Google Scholar 

  4. Cahill, M., Lambert, D., Pinheiro, J.: Detecting fraud in the real world. Handbook of Massive Datasets (2002)

    Google Scholar 

  5. Damerau, F.J.: A technique computer detection and correction of spelling errors. Communications of ACM 7(3), 171–176 (1964)

    Article  Google Scholar 

  6. Darmoul, S., Pierreval, H., Gabouj, S.: Scheduling using artificial immune system metaphors: A review. In: IEEE International Conference on Service Systems and Service Management, Troyes France, pp. 1150–1155 (2006)

    Google Scholar 

  7. Dasgupta, D., Forrest, S.: Artificial immune system in industrial application. In: International conference on Intelligent Processing and Manufacturing Material (IPMM), Honolulu, HI, vol. 1, pp. 257–267 (1999)

    Google Scholar 

  8. De Castro, L., Timmis, J.: Artificial immune systems: A new computational intelligence approach. Springer, Heidelberg (2002)

    Google Scholar 

  9. Farag, S.S., et al.: Nk receptors: Biology and clinical relevance. Blood 100(6), 1935–1947 (2002)

    Article  Google Scholar 

  10. Fred, K.G., Marengo, E.A., Devanery, A.J.: Time-reversal imaging with multiple signal classification considering multiple scattering between the targets. J. Acoust. Soc. Am. 115(6), 3042–3047 (2004)

    Article  Google Scholar 

  11. Garrett, S.M.: How do we evaluate artificial immune systems? Evolutionary Computation 13(2), 145–178 (2005)

    Article  Google Scholar 

  12. Goldsby, R., Kindt, T., Osborne, B.: Kuby Immunology, 4th edn. WH Freeman at MacMillan Press (2000)

    Google Scholar 

  13. Heerden, J.H.V.: Detection fraud in cellular telephone networks. Master’s thesis, Interdepartmental program of operational Analysis. University of Stellenbosch, South Africa (2005)

    Google Scholar 

  14. Hofmeyr, S.A.: An interpretative introduction to the immune system. In: Design Principles for the Immune System and Other Distributed Autonomous Systems ed., Oxford University Press, Oxford (2000)

    Google Scholar 

  15. Hofmeyr, S.A., Forrest, S.: Architecture for an artificial immune system. Journal of Evolutionary Computation 7(1), 45–68 (1999)

    Article  Google Scholar 

  16. Hollmen, J., Tresp, V.: Call-based fraud detection in mobile communication networks using a hierarchical regime-switching model. In: Kearns, M., Solla, S., Cohn, D. (eds.) P. of the Conference (NIPS 11). Advances in Neural Information Processing Systems 11, pp. 889–895. MIT Press, Cambridge (1998)

    Google Scholar 

  17. Hyyrö, H.: A bit-vector algorithm for computing levenshtein and damerau edit distances Nord. J. Comput. 10(1), 29–39 (2003)

    MATH  MathSciNet  Google Scholar 

  18. Jerne, N.K.: Towards a Network Theory of the Immune System, vol. 125C, pp. 373–389. Annales d’Immunologie (1974)

    Google Scholar 

  19. Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons, Chichester (2003)

    MATH  Google Scholar 

  20. Lee, R.B., Shi, Z., Yang, X.: Efficient permutation instructions for fast software cryptography. IEEE Micro. 21(6), 56–69 (2001)

    Article  Google Scholar 

  21. Luh, G.-C., Liu, W.W.: An immunological approach to mobile robot reactive navigation. Applied Soft Computing Journal (2006); 10.1016/j.asoc.2006.10.009

    Google Scholar 

  22. Madisetti, V.V., Douglass, B.: The digital signal processing handbook. IEEE Press, CRC Press (1988)

    Google Scholar 

  23. Matzinger, P.: The danger model in its historical context. Scandinavian Journal of Immunology 54, 4–9 (2001)

    Article  Google Scholar 

  24. Menezes, A., van Oorschot, P., Vanstone, S.: Handbook of Applied cryptography. CRC Press, Inc., Boca Raton (1996)

    Google Scholar 

  25. Twycross, J., Aickelin, U.: Towards a conceptual framework for innate immunity. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 112–125. Springer, Heidelberg (2005)

    Google Scholar 

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Roger Lee

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Elmeziane, R., Berrada, I., Kassou, I. (2008). A New Artificial Immune System for the Detection of Abnormal Behaviour. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70560-4_10

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  • DOI: https://doi.org/10.1007/978-3-540-70560-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70559-8

  • Online ISBN: 978-3-540-70560-4

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