Abstract
The Artificial Immune Systems (AIS) constitute an emerging and very promising area of research that historically have been falling within two main theoretical immunological schools of thought: those based on Negative selection (NS) or those inspired on Danger theory (DT). Despite their inherent strengths and well known promising results, both deployed AIS have documented difficulties on dealing with gradual dynamic changes of self behavior through time.
In this paper we propose and describe the development of an AIS framework for anomaly detection based on a rather different immunological theory, which is the Grossman’s Tunable Activation Thresholds (TAT) theory for the behaviour of T-cells. The overall framework has been tested with artificially generated stochastic data sets based on a real world phenomena and the results thus obtained have been compared with a non-evolutionary Support Vector Machine (SVM) classifier, thus demonstrating TAT’s performance and competitiveness for anomaly detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Murphy, K., Murphy, K., Travers, P., Walport, M., Janeway, C.: Janeway’s immunobiology. Garland Pub. (2008)
Burnet, F.: The Clonal Selection Theory of Acquired Immunity. University Press Nashville, Tenn (1959)
Matzinger, P.: Tolerance, danger, and the extended family. Annual Review of Immunology 12(1), 991–1045 (1994)
Aickelin, U., Greensmith, J.: Sensing danger: Innate immunology for intrusion detection. Information Security Technical Report 12(4), 218–227 (2007)
de Castro, L., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer (2002)
Hofmeyr, S., Forrest, S.: Architecture for an artificial immune system. Evolutionary Computation 8(4), 443–473 (2000)
Kim, J., Bentley, P., Aickelin, U., Greensmith, J., Tedesco, G., Twycross, J.: Immune system approaches to intrusion detection - a review. Natural Computing 6(4), 413–466 (2007)
Greensmith, J., Aickelin, U., Cayzer, S.: Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 153–167. Springer, Heidelberg (2005)
Stibor, T., Mohr, P., Timmis, J., Eckert, C.: Is negative selection appropriate for anomaly detection? In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 321–328 (2005)
Kim, J., Bentley, P.: An evaluation of negative selection in an artificial immune system for network intrusion detection. In: Genetic and Evolutionary Computation Conference, pp. 1330–1337 (2001)
Andrews, P., Timmis, J.: Tunable Detectors for Artificial Immune Systems: From Model to Algorithm. In: Bioinformatics for Immunomics, pp. 103–127 (2010)
Stepney, S., Smith, R., Timmis, J., Tyrrell, A., Neal, M., Hone, A.: Conceptual frameworks for artificial immune systems. International Journal of Unconventional Computing 1(3), 315–338 (2005)
Grossman, Z.: Cellular tolerance as a dynamic state of the adaptable lymphocyte. Immunology Reviews 133, 45–73 (1993)
Grossman, Z., Paul, W.: Self-tolerance: context dependent tuning of T cell antigen recognition. Seminars in Immunology 12(3), 197–203 (2000)
Scherer, A., Noest, A., de Boer, R.: Activation-threshold tuning in an affinity model for the T-cell repertoire. In: Proceedings: Biological Sciences, vol. 271(1539), pp. 609–616 (2004)
Carneiro, J., Paixão, T., Milutinovic, D., Sousa, J., Leon, K., Gardner, R., Faro, J.: Immunological self-tolerance: Lessons from mathematical modeling. Journal of Computational and Applied Mathematics 184(1), 77–100 (2005)
Helton, J., Davis, F.: Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety 81(1), 23–69 (2003)
Metsis, V., Androutsopoulos, I., Paliouras, G.: Spam filtering with naive bayes-which naive bayes. In: Third Conference on Email and Anti-Spam (CEAS), pp. 125–134 (2006)
Abi-Haidar, A., Rocha, L.: Adaptive Spam Detection Inspired by the Immune System. In: Artificial Life XI - 11th Int. Conference on the Simulation Ans Synthesis Os Living Systems, vol. 11, pp. 1–8 (2008)
Silva, C., Ribeiro, B.: Inductive Inference for Large Scale Text Classification: Kernel Approaches and Techniques. Springer (2009)
Antunes, M., Correia, M.: Temporal Anomaly Detection: an Artificial Immune Approach Based on T-cell Activation, Clonal Size Regulation and Homeostasis. Advances in Computational Biology - Book series vol. 680, pp. 291–298 (2010)
Antunes, M., Correia, M.: TAT-NIDS: an immune-based anomaly detection architecture for network intrusion detection. In: Corchado, J.M., et al. (eds.) IWPACBB 2008. ASC, vol. 49, pp. 60–67. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Antunes, M.J., Correia, M.E. (2012). Self Tolerance by Tuning T-Cell Activation: An Artificial Immune System for Anomaly Detection. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-32615-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32614-1
Online ISBN: 978-3-642-32615-8
eBook Packages: Computer ScienceComputer Science (R0)