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Discriminative power of the receptors activated by k-contiguous bits rule
Keywords:
Binary Immune System, Schemas, Binary Receptors, Detection Probability, Lower Bounds on Failure Probability, Maximal DetectabilityAbstract
The paper provides a brief introduction into a relatively new discipline: artificial immune systems (AIS). These are computer systems exploiting the natural immune system (or NIS for brevity) metaphor: protect an organism against invaders. Hence, a natural field of applications of AIS is computer security. But the notion of invader can be extended further: for instance a fault occurring in a system disturbs patterns of its regular functioning. Thus fault, or anomaly detection is another field of applications. It is convenient to represent the information about normal and abnormal functioning of a system in binary form (e.g. computer programs/viruses are binary files). Now the problem can be stated as follows: given a set of self patterns representing normal behaviour of a system under considerations find a set of detectors (i.e, antibodies, or more precisely, receptors) identifying all non self strings corresponding to abnormal states of the system. A new algorithm for generating antibody strings is presented. Its interesting property is that it allows to find in advance the number of of strings which cannot be detected by an "ideal" receptors repertoire.
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References
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Computer Applications, 19, 189-212
[17] Hunt, J.E., Cooke, D.E., and Holstein, H. (1995) Case memory and retrieval based on the immune system. In: W.
Weloso and A. Aamodt (eds.) Case-Based Reasoning Research development, LNAI 1010, Springer-Verlag,
pp. 205-216
[18] Hunt, J.E., et al. (1998) Jisys: The development of an artificial immune system for real word applications. In
(Dasupta, 1998), pp. 157-186
[19] Jerne, N.K. (1974) Towards a network theory of the immune system. Ann. Immunol. (Inst. Pasteur), 125C: 373-
389
[20] Kephart, J.O. (1994). A biologically inspired immune system for computers. In: R.A. Brooks and P. Maes (eds.),
Proceedings of the Fourth International Workshop on Synthesis and Simulation of Living Systems, Cambridge, MA, pp. 130-139.
[21] Oprea, M.L. (1999) Antibody repertoires and pathogen recognition: The role of germline diversity and somatic hypermutation. Ph. D. Thesis, The University of New Mexico, Albuquerque, New Mexico.
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combining region from consideration of efficient self-non-self discrimination. Proc. Natl. Acad. Sci. USA, 90: 1691-1695
[23] Perelson, A.S., and Weisbuch, G. (1997) Immunology for physicists. Reviews of Modern Physics, 69: 1219-1265
[24] Smith, R.E., Forrest, S., and Perelson, A.S. (1993) Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation 1(2): 127-149
[2] Dasgupta, D. (1987) Artificial neural networks and artificial immune systems: similarities and differences, in: Proc. of the IEEE International Conference on Systems man and Cybernetics, Orlando, FL, pp.873-878
[3] Dasgupta, D. (1998) Artificial Immune Systems and Their Applications. Springer-Verlag: Berlin Heidelberg
[4] Dasgupta, D. (1999) Immunity-based intrusion detection systems: A general framework. In: Proc. of the 22-nd National Information Systems Security Conference, October 18-21.
[5] Dasgupta, D., and Forrest, S. (1996) Novelty detection in time series data using ideas from immunology. In:
ISCA 5th International Conf. on Intelligent Systems, Reno, Nevada (also in (Dasgupta, 1998), pp. 262-267)
[6] Dasgupta, D., cao, Y., and Y D’haeseleer, P., Forrest, S., and Helman, P. (1996). An immunological approach to change detection: algorithms, analysis, and implications. In Proc. of IEEE Symposium on Research in Security and Privacy, Oakland, CA
[7] Farmer, J.D., Packard, N.H. and Perelson, A.S. (1986) The immune system, adaptation, and machine learning.
Physica D, 22: 187-204
[8] Farmer, J.D. (1990) A rosetta stone for connectionism. Physica D, 42: 153-187
[9] Forrest, S., Perelson, A.S. (1990) Generic algorithms and the immune system. In: H.P. Schwefel and H. Muhlenbein (eds.) Parallel Problem Solving from Nature, LNCS 496, Springer-Verlag, pp. 343-354
[10] Forrest, S., Perelson, A.S., Allen, L., and Cherukuri, R. (1994). Self-non-self discrimination in a computer. In
Proc. of 1994 IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA: IEEE Computer Society Press
[11] Gaspar, A., and Coolard, Ph. (1999) From Gas to artificial immune systems: Improving adaptation in time dependent optimization. In: Proc. 1999 Congress on Evolutionary Computation. July 6-9, Mayflower Hotel, Washington D.C., vol. 3, pp. 1859-1866
[12] Hart, E., Ross, P., and Nelson, J. (1998) Producing robust schedules via an artificial immune system. In: IEEE
World Congress on Computational Intelligence, International Conference on Evolutionary Computing
[13] Hightower, R., Forrest, S., and Perelson, A.S. (1995) The evolution of emergent organization in immune system
gene libraries. In: L.J. Eshelman (ed.) Proc. of the 6-th International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, CA, pp. 344-350
[14] Hoffmann, G.W. (1986) A neural model based on the analogy with the immune system. Journal of Theoretical
Biology, 122: 33-67
[15] Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press: ANN Arbour.
[16] Hunt, J.E., and Cooke, D.E. (1996) Learning using an artificial immune system. Journal of Network and
Computer Applications, 19, 189-212
[17] Hunt, J.E., Cooke, D.E., and Holstein, H. (1995) Case memory and retrieval based on the immune system. In: W.
Weloso and A. Aamodt (eds.) Case-Based Reasoning Research development, LNAI 1010, Springer-Verlag,
pp. 205-216
[18] Hunt, J.E., et al. (1998) Jisys: The development of an artificial immune system for real word applications. In
(Dasupta, 1998), pp. 157-186
[19] Jerne, N.K. (1974) Towards a network theory of the immune system. Ann. Immunol. (Inst. Pasteur), 125C: 373-
389
[20] Kephart, J.O. (1994). A biologically inspired immune system for computers. In: R.A. Brooks and P. Maes (eds.),
Proceedings of the Fourth International Workshop on Synthesis and Simulation of Living Systems, Cambridge, MA, pp. 130-139.
[21] Oprea, M.L. (1999) Antibody repertoires and pathogen recognition: The role of germline diversity and somatic hypermutation. Ph. D. Thesis, The University of New Mexico, Albuquerque, New Mexico.
[22] Percus, J.K., Percus, O.E., and Perelson, A.S. (1993). Predicting the size of the T-cell receptor and antibody
combining region from consideration of efficient self-non-self discrimination. Proc. Natl. Acad. Sci. USA, 90: 1691-1695
[23] Perelson, A.S., and Weisbuch, G. (1997) Immunology for physicists. Reviews of Modern Physics, 69: 1219-1265
[24] Smith, R.E., Forrest, S., and Perelson, A.S. (1993) Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation 1(2): 127-149
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Published
2000-10-02
How to Cite
Wierzchon, S. T. (2000). Discriminative power of the receptors activated by k-contiguous bits rule. Journal of Computer Science and Technology, 1(03), 14 p. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/1007
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