A New Artificial Immune System Based on the Principle of Antibody Diversity And Antigen Presenting Cell

Antibody Diversity 원리와 Antigen Presenting Cell을 구현한 새로운 인공 면역 시스템

  • 이상형 (연세대학교 전기전자공학부) ;
  • 김은태 (연세대학교 전기전자공학) ;
  • 박민용 (연세대학교 전기전자공학부)
  • Published : 2004.07.01

Abstract

This paper proposes a new artificial immune approach to on-line hardware test which is the most indispensable technique for fault tolerant hardware. A novel algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity. Tolerance conditions in artificial immune system correspond to the antibody in biological immune system. In addition, antigen presenting cell (APC) is realized by Quine-McCluskey method in this algorithm and tolerance conditions are generated through GA (Genetic Algorithm). The suggested method is applied to the on-line monitoring of a typical FSM (a decade counter) and its effectiveness is demonstrated by the computer simulation.

본 논문에서는 fault tolerant 하드웨어에서 가장 기본이 되는 온라인 하드웨어 테스트 시스템 구현을 위하여 새로운 인공면역 알고리즘을 제안한다. 인공 면역 알고리즘은 알려진 자기(self) 정보만을 이용하여 항체 즉 tolerance condition을 가장 최적으로 생성하는 알고리즘이다. 이를 위하여 본 논문에서는 생체 면역 시스템의 중요한 원리인 antibody diversity 원리를 적용한 새로운 tolerance condition 생성 알고리즘을 제안한다. 또한 생체 면역 시스템에서의 중요한 세포인 APC (Antigen Presenting Cell)를 Quine-McCluskey 방법으로 구현한 후 유전자 알고리즘을 통해 tolerance condition을 자동 생성하는 알고리즘을 구현한다. 이렇게 제안된 알고리즘은 FSM(Finite State Machine)의 가장 전형적인 예인 십진카운터에 적용한 후 컴퓨터 모의 실험을 통해 그 성능을 확인한다.

Keywords

References

  1. Y. Chen and T. Chen, 'Implementing fault-tolerance via modular redundancy with comparison,' IEEE Transactions on Reliability, Vol. 39, Issue: 2 , Jun.1900, pp. 217 -225 https://doi.org/10.1109/24.55885
  2. S. Dutt .and N.R. Mahapatra, 'Node-covering, error -correcting co -des and multiprocessors with very high average fault tolerance,' IEEE Trans. Comput., Vol. 46, Sep.1997, pp.997-1914 https://doi.org/10.1109/12.620481
  3. P. K. Lala, Digital Circuit Testing and Testablilty, New York: Academic, 1997
  4. P. K. Harmer, P. D.Williams, G. H. Grunsch, and G. B.Lamont, 'An Artificial Immune System Architecture For Computer Security Applications,' IEEE Transactions on Evolutionary Computation, Vol.6, No.3, Jun. 2002, pp. 252-280 https://doi.org/10.1109/TEVC.2002.1011540
  5. S. Forrest, S.A. Hofmeyr, A. Somayaji, and T.A. Longstaff, 'A Sense of Self for Unix Processing,' Proc.IEEE Symp, Computer Security and Privacy, May, 1900, pp.120-128 https://doi.org/10.1109/SECPRI.1996.502675
  6. S.Forrest, L.Allen, A.S. Perelson, and R.Cheru -kuri, 'Self-Nonself Discrimination In A Computer,' Proceedings of IEEE Symposium on Research in Security and Privacy, 1994, pp.202-212 https://doi.org/10.1109/RISP.1994.296580
  7. D.Dasgupta, 'An artificial immune system as a multi-agent decision support system,' Proc. IEEE Int. Conf. Systems, Man and Cybernetics, Oct. 1998, pp.3816-3820 https://doi.org/10.1109/ICSMC.1998.726682
  8. S.A. Hofmeyr and S. Forest, 'Architecture for an artificial immune system,' Evol.Comput.,vol.8, no. 4, pp.443-473 https://doi.org/10.1162/106365600568257
  9. P.D'haeseller, S. Forrest, P. Helman, 'An Immunological Approach to Change Detection: Alogorithms, Analysis and Implications,' Proc. Of IEEE Symp. On Security and Privacy, 1996 https://doi.org/10.1109/SECPRI.1996.502674
  10. R.A. Goldsby, T.J. Kindt, and B.A Osborne, Kuby Immunology, 4th ed. W.H Freeman and Company: New York, 2000
  11. Roitt I, Brostoff J, Male DK. Immunology. 5th ed. St Louis, MOL Mosby International Limited, 1998
  12. D.W. Bradley and AM. Tyrrell, 'Immunotronics -Novel Finite-State-Machine Architectures With Built-In Self-Test Using Self-Nonself Differentiation,' IEEE Trans. On Evolutionary Computation, Vol.6, No.3, Jun. 2002, pp. 227-238 https://doi.org/10.1109/TEVC.2002.1011538
  13. D.E Goldberg, Genetic Algorithms in Search, Optimization and Matching Learning, Addison-Wesley:MA 1989