Conserved Self Pattern Recognition Algorithm

  • Senhua Yu
  • Dipankar Dasgupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)


Self-nonself model makes a lot of sense in the mechanisms of self versus nonself recognition in the immune system but it failed to explain a great number of findings. Some new immune theory is proposed to accommodate incompatible new findings, including Pattern Recognition Receptors (PRRs) Model and Danger Theory. Inspired from the PRRs model, a novel approach called Conserved Self Pattern Recognition Algorithm (CSPRA) is proposed in this paper. The algorithm is tested using the famous benchmark Fisher’s Iris data. Preliminary results demonstrate that the new approach lowers the false positive and thus enhances the efficiency and reliability for anomaly detection without increase in complexity comparing to the classical Negative Selection Algorithm (NSA).


Conserved Self Pattern Recognition Algorithm Pattern Recognition Receptors Model CSPRA Artificial Immune System 


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  1. 1.
    Dasgupta, D.: Advances in Artificial Immune System. IEEE computional Intelligence Magazine (2006)Google Scholar
  2. 2.
    Garrett, S.M.: How do we evaluate artificial immune systems? Evolutionary Computation 13(2), 145–178 (2005)CrossRefGoogle Scholar
  3. 3.
    Aickelin, U., Greensmith, J., Twycross, J.: Immune System Approaches to Intrusion Detection – A Review. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 316–329. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Burgess, M.: Computer immunology. In: Proc. of the Systems Administration Conference (LISA 1998), pp. 283–297 (1998)Google Scholar
  5. 5.
    Matzinger, P.: The danger model: a renewed sense of self. Science 296(5566), 301–305 (2002)CrossRefGoogle Scholar
  6. 6.
    Janeway Jr., C.A.: Approaching the asymptote? Evolution and revolution in immunology. In: Cold Spring Harbor Symp. Quant. Biol., vol. 54, pp. 1–13 (1989)Google Scholar
  7. 7.
    Janeway Jr., C.A.: The immune system evolved to discriminate infectious nonself from noninfectious self. Immunol. Today 13(1), 11–16 (1992)CrossRefGoogle Scholar
  8. 8.
    Medzhitov, R., Janeway Jr., C.A.: Decoding the patterns of self and nonself by the innate immune system. Science 296(5566), 298–300 (2001)CrossRefGoogle Scholar
  9. 9.
    Gomez, J., Gonzalez, F., Dasgupta, D.: An immuno-fuzzy approach to anomaly detection. In: proceedings of the 12th IEEE International Conference on Fuzzy Systems (FUZZIEEE), vol. 2, pp. 1219–1224 (2003)Google Scholar
  10. 10.
    Yeom, K.W.: Immune-inspired Algorithm for Anomaly Detection. In: Computational Intelligence in Information Assurance and Security. Studies in Computational Intelligence, vol. 57, pp. 129–154. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Koshland Jr., D.E.: Recognizing self from nonself. Science 248(4961), 1273 (1990)CrossRefGoogle Scholar
  12. 12.
    Aickelin, U., Cayzer, S.: The danger theory and its application to artificial immune systems. In: proceedings of The First International Conference on Artificial Immune Systems (ICARIS 2002), pp. 141–148 (2002)Google Scholar
  13. 13.
    Dasgupta, D., Yu, S., Majumdar, N.S.: MILA - multilevel immune learning algorithm. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 183–194. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Ji, Z., Dasgupta, D.: Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Senhua Yu
    • 1
  • Dipankar Dasgupta
    • 1
  1. 1.Department of Computer ScienceUniversity of MemphisMemphisUSA

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