Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30465
Danger Theory and Intelligent Data Processing

Authors: Mohd Aizaini Maarof, Anjum Iqbal

Abstract:

Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). Classically it is believed that IS strives to discriminate between self and non-self. Most of the existing AIS research is based on this approach. Danger Theory (DT) argues this approach and proposes that IS fights against danger producing elements and tolerates others. We, the computational researchers, are not concerned with the arguments among immunologists but try to extract from it novel abstractions for intelligent computation. This paper aims to follow DT inspiration for intelligent data processing. The approach may introduce new avenue in intelligent processing. The data used is system calls data that is potentially significant in intrusion detection applications.

Keywords: artificial immune system, danger theory, intelligent processing, system calls

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082947

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1494

References:


[1] A. Somayaji, S. Hofmeyr, and S. Forrest, "Principles of a Computer Immune System," In Proc. New Security Paradigms Workshop, Charlottsville, 1998, pp. 75-82
[2] L. N. de Castro, and F. J. V. Zuben, "Artificial Immune Systems: A Survey of Applications," State University of Campinas, SP, Brazil, Tech. Rep. DCA - RT 01/99, 1999, Part I.
[3] L. N. de Castro, and F. J. V. Zuben, "Artificial Immune Systems: A Survey of Applications," State University of Campinas, SP, Brazil, Tech. Rep. DCA - RT 02/00, 2000, Part II.
[4] U. Aickelin, and S. Cayzer, "The Danger Theory and Its Application to Artificial Immune Systems," In Proc. First International Conference on Artificial Immune Systems (ICARIS-2002), Canterbury, UK, 2002.
[5] A. Iqbal, and M. A. Maarof, "Distinct Viewpoints in Novel Biologically Inspired Computational Research: Artificial Immune Systems," In Proc. Second International Conference on Artificial Intelligence Applications in Engineering and Technology, Malaysia, 2004.
[6] S. A. Hofmeyr, and S. Forrest, "Architecture for an Artificial Immune System," Evolutionary Computation Journal, vol. 8 no. 4, pp. 443-473, 2000.
[7] J. Kim, "Computers are from Mars, Organisms are from Venus," IEEE Computer, vol. 35 no. 7, pp. 25-32, 2002.
[8] A. Iqbal, and M. A. Maarof, "Artificial Immune System and Immunoinformatics: Bridging Computation and Immunology," In Proc. Kuala Lumpur International Conference on Biomedical Engineering (BioMed 2004), Malaysia, 2004.
[9] http://www.cs.unm.edu/~immsec/data-sets.htm
[10] A. Iqbal, and M. A. Maarof, "Towards Danger Theory based Artificial APC Model: Novel Metaphor for Danger Susceptible Data Codons," Italy: Springer-Verlag, 2004, ch. 13.
[11] P. Matzinger, "The Danger Model: A Renewed Sense of Self," Science, vol. 296, pp. 301-305, 2002.
[12] P. Matzinger, "The Danger Model in Its Historical Context," Scand. J. Immunol, vol. 54, pp. 4-9, 2001.
[13] S. Gallucci, M. Lolkema, and P. Matzinger (1999), Natural Adjuvants: Endogenous Activators of Dendritic Cells," Nature Medicine, vol. 5, no. 11, pp. 1249-1255, 1999.
[14] P. Matzinger, "The Real Function of the Immune System," Available: URL:http://cmmg.biosci.wayne.edu/asg/polly.html, 06-04-04.
[15] P. Matzinger, An Innate sense of danger, Seminars in Immunology, vol. 10, pp. 399-415, 1998.