A Review of Artificial Immune Systems

Zafer Ataser

Abstract

Artificial Immune Systems (AIS) are class of computational intelligent methods developed based on the principles and processes of the biological immune system. AIS methods are categorized mainly into four types according to the inspired principles and processes of immune system. These categories are clonal selection, negative selection, immune network and danger theory. This paper reviews the models of AIS and the progress of them. The fundamental characteristics of AIS models are identified and some major studies of each model are given. In addition to that, some application areas of AIS models are explained.

References

  1. Aickelin, U. and Cayzer, S. (2002). The danger theory and its application to artificial immune systems. In In Proceedings of the 1st International Conference on Artificial Immune Systems. Springer-Verlag.
  2. Aickelin, U. and Greensmith, J. (2007). Sensing danger: Innate immunology for intrusion detection. In Information Security Technical Report. Elsevier.
  3. Al-Enezi, Abbod, J., and Alsharhan, M. (2010). Artificial immune systems - models, algorithms and application. In International Journal of Research and Reviews in Applied Sciences. ARPA Press.
  4. Balachandran, S., Dasgupta, D., Nino, F., and Garrett, D. (2007). A framework for evolving multi-shaped detectors in negative selection. In In Proceedings of the 2007 IEEE symposium on foundations of computational intelligence. IEEE Xplore.
  5. Bezerra, G. B., Barra, T. V., de Castro, L. N., and Zuben, F. J. V. (2005). Adaptive radius immune algorithm for data clustering. In Artificial Immune Systems: 4th International Conference. Springer-Verlag.
  6. Brownlee, J. (2005). Clonal selection theory and clonalg - the clonal selection classification algorithm (csca). In Centre for Intelligent Systems and Complex Processes (CISCP), Tech. Rep. 2-02. Faculty of Information and Communication Technologies (ICT), Swinburne University of Technology.
  7. Chen, J., Liang, F., and Yang, D. (2005). Dynamic negative selection algorithm based on match range model. In Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence. SpringerVerlag.
  8. Coelho, G. P. and Zuben, F. J. V. (2010). A concentrationbased artificial immune network for continuous optimization. In IEEE Congress on Evolutionary Computation. IEEE.
  9. Coelho, G. P. and Zuben, F. J. V. (2011). A concentrationbased artificial immune network for multi-objective optimization. In International Conference on Evolutionary Multi-Criterion Optimization. SpringerVerlag.
  10. Dasgupta, D. and Gonzalez, F. (2002). An immunity-based technique to characterize intrusions in computer networks. In IEEE Transactions on Evolutionary Computation. IEEE Press.
  11. Dasgupta, D. and Nino, F. (2000). A comparison of negative and positive selection algorithms in novel pattern detection. In IEEE International Conference on Systems, Man, and Cybernetics. IEEE Xplore.
  12. de Castro, L. N. and Zuben, F. J. V. (1999). Artificial immune systems: part i - basic theory and applications. In Technical Report DCA-RT 01/99. School of Computing and Electrical Engineering, State University of Campinas.
  13. de Castro, L. N. and Zuben, F. J. V. (2000). The clonal selection algorithm with engineering applications. In Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann.
  14. de Castro, L. N. and Zuben, F. J. V. (2001). ainet: An artificial immune network for data analysis. In In Data Mining: A Heuristic Approach. Idea Group.
  15. de Castro, L. N. and Zuben, F. J. V. (2002). Learning and optimization using the clonal selection principle. In IEEE Transactions on Evolutionary Computation. IEEE Press.
  16. Forrest, S., Perelson, A., Allen, L., and Cherukuri, R. (1994). Self-nonself discrimination in a computer. In In Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. IEEE Computer Society.
  17. Freitas, A. A. and Timmis, J. (2007). Revisiting the foundations of artificial immune systems for data mining. In IEEE Transactions on Evolutionary Computation. IEEE Press.
  18. Garrett, S. M. (2005). How do we evaluate artificial immune systems? In Evolutionary Computation. MIT Press.
  19. Gonzalez, F. and Dasgupta, D. (2002). An immunogenetic technique to detect anomalies in network traffic. In In Proceedings of the genetic and evolutionary compuation conference. Morgan Kaufmann.
  20. Gonzalez, F. and Dasgupta, D. (2003). Anomaly detection using real-valued negative selection. In Genetic Programming and Evolvable Machines. Kluwer Academic.
  21. Gonzalez, F., Dasgupta, D., and Gomez, J. (2003a). The effect of binary matching rules in negative selection. In In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003). SpringerVerlag.
  22. Gonzalez, F., Dasgupta, D., and Kozma, R. (2002). Combining negative selection and classification techniques for anomaly detection. In Proceedings of the 2002 Congress on Evolutionary Computation. IEEE Computer Society.
  23. Gonzalez, F., Dasgupta, D., and Nino, L. F. (2003b). A randomized real-valued negative selection algorithm. In In Proceedings of the 2nd International Conference on Artificial Immune Systems. Springer-Verlag.
  24. Greensmith, J., Aickelin, U., and Twycross, J. (2004). Detecting danger: Applying a novel immunological concept to intrusion detection systems. In Proceedings of the 6th International Conference in Adaptive Computing in Design and Manufacture. Springer-Verlag.
  25. Hart, E. and Timmis, J. (2008). Application areas of ais: The past, the present and the future. In Applied Soft Computing. Elsevier Science.
  26. Hofmeyr, S. A. and Forrest, S. (1999). Immunity by design: An artificial immune system. In In Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann.
  27. Hofmeyr, S. A. and Forrest, S. (2000). Architecture for an artificial immune system. In Evolutionary Computation Journal. MIT Press.
  28. Jerne, N. (1974). Towards a network theory of the immune system. In Annals of Immunology. Inst. Pasteur.
  29. Ji, Z. and Dasgupta, D. (2004). Real-valued negative selection algorithm with variable-sized detectors. In In proceeding of Genetic and Evolutionary Computation. Springer-Verlag.
  30. Ji, Z. and Dasgupta, D. (2005). A boundary-aware negative selection algorithm. In In Proceedings of the international conference on artificial intelligence and soft computing. ACRA Press.
  31. Ji, Z. and Dasgupta, D. (2007). Revisiting negative selection algorithms. In MIT Evolutionary Computation. MIT Press.
  32. Ji, Z. and Dasgupta, D. (2009). V-detector: An efficient negative selection algorithm with 'probably adequate' detector coverage. In Information Sciences. Elsevier Science.
  33. Kim, J. and Bentley, P. J. (2001). An evaluation of negative selection in an artificial immune system for network intrusion detection. In Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann.
  34. Kim, J., Greensmith, J., Twycross, J., and Aickelin, U. (2005). Malicious code execution detection and response immune system inpired by the danger theory. In Proceedings of the Adaptive and Resilient Computing Security Workshop (ARCS-05).
  35. L. O. V. B. Oliveira, R. L. M. Motay, D. A. C. B. (2012). Clonal selection classifier with data reduction: Classification as an optimization task. In IEEE World Congress on Computational Intelligence. IEEE.
  36. Li, J., Gao, H., and Wang, S. (2012). A novel clone selection algorithm with reconfigurable search space ability and its application. In Fourth International Conference on Natural Computation. IEEE Computer Society.
  37. Liu, L. and Xu, W. (2008). A cooperative artificial immune network with particle swarm behavior for multimodal function optimization. In IEEE Congress on Evolutionary Computation. IEEE Press.
  38. Matzinger, P. (2002). The danger model: A renewed sense of self. In Science.
  39. Powers, S. T. and He, J. (2006). Evolving discrete-valued anomaly detectors for a network intrusion detection system using negative selection. In In the 6th Annual Workshop on Computational Intelligence (UKCI 7806). University of Leeds.
  40. Roper, M. (2009). Artifical immune systems, danger theory, and the oracle problem. In Testing: Academic and Industrial Conference - Practice and Research Techniques. IEEE Computer Society.
  41. Stibor, T., Mohr, P., Timmis, J., and Eckert, C. (2005a). Is negative selection appropriate for anomaly detection? In Proceedings of the 2005 conference on Genetic and evolutionary computation. ACM.
  42. Stibor, T., Timmis, J., and Eckert, C. (2005b). A comparative study of real-valued negative selection to statistical anomaly detection techniques. In Proceedings of the 4th international conference on Artificial Immune Systems. Springer-Verlag.
  43. Timmis, J. and Neal, M. (2001). A resource limited artificial immune system for data analysis. In Knowledge Based Systems. Elsevier.
  44. Timmis, J., Neal, M., and Hunt, J. (2000). An artificial immune system for data analysis. In Biosystems. Elsevier.
  45. X. Yuel, F. Zhang, L. X. and Wangl, D. (2010). Optimization of self set and detector generation base on real-value negative selection algorithm. In 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering. IEEE Xplore.
  46. Zeng, J., Liu, X., Li, T., Liu, C., Peng, L., and Sun, F. (2009). A self-adaptive negative selection algorithm used for anomaly detection. In Progress in Natural Science. Elsevier.
  47. Zhong, Y. and Zhang, L. (2012). An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery. In IEEE Transactions on Geoscience and Remote Sensing. IEEE.
  48. Zhu, Y. and Tan, Y. (2011). A danger theory inspired learning model and its application to spam detection. In Proceedings of the second international conference on advances in swarm intelligence. Springer-Verlag.
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Paper Citation


in Harvard Style

Ataser Z. (2013). A Review of Artificial Immune Systems . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 128-135. DOI: 10.5220/0004553101280135


in Bibtex Style

@conference{ecta13,
author={Zafer Ataser},
title={A Review of Artificial Immune Systems},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)},
year={2013},
pages={128-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004553101280135},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)
TI - A Review of Artificial Immune Systems
SN - 978-989-8565-77-8
AU - Ataser Z.
PY - 2013
SP - 128
EP - 135
DO - 10.5220/0004553101280135