Abstract
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).
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References
Dasgupta, D.: Advances in Artificial Immune System. IEEE computional Intelligence Magazine (2006)
Garrett, S.M.: How do we evaluate artificial immune systems? Evolutionary Computation 13(2), 145–178 (2005)
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)
Burgess, M.: Computer immunology. In: Proc. of the Systems Administration Conference (LISA 1998), pp. 283–297 (1998)
Matzinger, P.: The danger model: a renewed sense of self. Science 296(5566), 301–305 (2002)
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)
Janeway Jr., C.A.: The immune system evolved to discriminate infectious nonself from noninfectious self. Immunol. Today 13(1), 11–16 (1992)
Medzhitov, R., Janeway Jr., C.A.: Decoding the patterns of self and nonself by the innate immune system. Science 296(5566), 298–300 (2001)
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)
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)
Koshland Jr., D.E.: Recognizing self from nonself. Science 248(4961), 1273 (1990)
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)
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)
Iris Data Set, http://archive.ics.uci.edu/ml/datasets/Iris
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)
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Yu, S., Dasgupta, D. (2008). Conserved Self Pattern Recognition Algorithm. In: Bentley, P.J., Lee, D., Jung, S. (eds) Artificial Immune Systems. ICARIS 2008. Lecture Notes in Computer Science, vol 5132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85072-4_25
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DOI: https://doi.org/10.1007/978-3-540-85072-4_25
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