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Temporal Anomaly Detection: An Artificial Immune Approach Based on T Cell Activation, Clonal Size Regulation and Homeostasis

Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 680)

Abstract

This paper presents an artificial immune system (AIS) based on Grossman’s tunable activation threshold (TAT) for temporal anomaly detection. We describe the generic AIS framework and the TAT model adopted for simulating T Cells behaviour, emphasizing two novel important features: the temporal dynamic adjustment of T Cells clonal size and its associated homeostasis mechanism. We also present some promising results obtained with artificially generated data sets, aiming to test the appropriateness of using TAT in dynamic changing environments, to distinguish new unseen patterns as part of what should be detected as normal or as anomalous. We conclude by discussing results obtained thus far with artificially generated data sets.

Keywords

Artificial immune systems Pattern recognition Anomaly detection Homeostasis 

Notes

Acknowledgements

The authors acknowledge the facilities provided by the CRACS research unit, an INESC associate of the Faculty of Science, University of Porto.

References

  1. 1.
    Antunes M, Correia E (2008) TAT-NIDS: an immune-based anomaly detection architecture for network intrusion detection, Proceedings of IWPACBB’08 – Advances in Soft Computing (Springer), pp 60–67Google Scholar
  2. 2.
    Antunes M, Correia E, Carneiro J (2009) Towards an immune-inspired temporal anomaly detection algorithm based on tunable activation thresholds, Proceedings of International Conference of Bioinspired systems and signal processing (BIOSIGNALS), pp 357–362Google Scholar
  3. 3.
    Antunes M, Correia E (2009) An Artificial Immune System for Temporal Anomaly Detection Using Cell Activation Thresholds and Clonal Size Regulation with Homeostasis, Proceedings of International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS), pp 323–326Google Scholar
  4. 4.
    Burmester G, Pezzuto A (2003) Color Atlas of Immunology. Thieme Medical Publishers, George Thieme VerlagGoogle Scholar
  5. 5.
    Burnet F (1959) The Clonal Selection Theory of Acquired Immunity, Vanderbilt University Press Nashville, TennesseGoogle Scholar
  6. 6.
    Carneiro J, Paixão T et al (2005) Immunological self-tolerance: Lessons from mathematical modeling, Journal of Computational and Applied Mathematics 184(1):77–100CrossRefGoogle Scholar
  7. 7.
    Castro L, Timmis J (2002) Artificial Immune Systems: A New Computational Intelligence Approach. Springer, New YorkGoogle Scholar
  8. 8.
    Grossman Z and Singer A (1996), Tuning of activation thresholds explains flexibility in the selection and development of Tcells in the thymus, Proceedings of the National Academy of Sciences 93(25):14747–14752CrossRefGoogle Scholar
  9. 9.
    Kim J, Bentley P (2001) An evaluation of negative selection in an artificial immune system for network intrusion detection, Proceedings of Genetic and Evolutionary Computation Conference (GECCO), 1330–1337Google Scholar
  10. 10.
    Kim J, Bentley P, Aickelin U, Greensmith J, Tedesco G, and Twycross J (2007) Immune system approaches to intrusion detection – a review, Natural Computing 6(4):413–466CrossRefGoogle Scholar
  11. 11.
    Matzinger P (2002) The danger model: a renewed sense of self, Science’s STKE 296(5566):301–305Google Scholar
  12. 12.
    Pedroso J (2007) Simple Metaheuristics Using the Simplex Algorithm for Non-linear Programming, Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics – LNCS (Springer), 4638:217–221Google Scholar
  13. 13.
    Stibor T, Timmis J and Eckert C (2005) On the appropriateness of negative selection defined over hamming shape-space as a network intrusion detection system, Proceedings of IEEE congress on Evolutionary Computation (CEC) 2:995–1002Google Scholar
  14. 14.
    Vance R (2000) Cutting edge commentary: A Copernican revolution? doubts about the danger theory, The Journal of Immunology 165:1725–1728PubMedGoogle Scholar
  15. 15.
    van den Berg H and Rand D (2004) Dynamics of T cell activation threshold tuning, Journal of Theoretical Biology 228(3):397–416PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Technology and ManagementPolytechnic Institute of LeiriaLeiriaPortugal

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