Application of the feature-detection rule to the Negative Selection Algorithm

The Negative Selection Algorithm developed by Forrest et al. was inspired by the way in which T-cell lymphocytes mature within the thymus before being released into the blood system. The mature T-cell lymphocytes exhibit an interesting characteristic, in that they are only activated by non-self cells that invade the human body. The Negative Selection Algorithm utilises an affinity matching function to ascertain whether the affinity between a newly generated (NSA) T-cell lymphocyte and a self-cell is less than a particular threshold; that is, whether the T-cell lymphocyte is activated by the self-cell. T-cell lymphocytes not activated by self-sells become mature T-cell lymphocytes. A new affinity matching function termed the feature-detection rule is introduced in this paper. The feature-detection rule utilises the interrelationship between both adjacent and non-adjacent features of a particular problem domain to determine whether an antigen is activated by an artificial lymphocyte. The performance of the feature-detection rule is contrasted with traditional affinity matching functions, currently employed within Negative Selection Algorithms, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming distance rule. This paper shows that the feature-detection rule greatly improves the detection rates and false alarm rates exhibited by the NSA (utilising the r-chunks and hamming distance rule) in addition to refuting the way in which permutation masks are currently being applied in artificial immune systems.

[1]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[2]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[3]  N K Jerne,et al.  Towards a network theory of the immune system. , 1973, Annales d'immunologie.

[4]  Claudia Eckert,et al.  Is negative selection appropriate for anomaly detection? , 2005, GECCO '05.

[5]  Jerne Nk.  Towards a network theory of the immune system. , 1974 .

[6]  Melvin Cohn,et al.  An alternative to current thinking about positive selection, negative selection and activation of T cells , 2004, Immunology.

[7]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[8]  Paul Helman,et al.  An immunological approach to change detection: algorithms, analysis and implications , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[9]  Rogério de Lemos,et al.  Negative Selection: How to Generate Detectors , 2002 .

[10]  Peter J. Bentley,et al.  An evaluation of negative selection in an artificial immune system for network intrusion detection , 2001 .

[11]  S T Lhu,et al.  Discriminative power of the receptors activated by k-contiguous bits rule , 2000 .

[12]  Xiang Zhang,et al.  Vector computer 757 , 1986, Journal of Computer Science and Technology.

[13]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[14]  J. Uspensky,et al.  Introduction to Mathematical Probability , 1938, Nature.

[15]  D. Dasgupta,et al.  Advances in artificial immune systems , 2006, IEEE Computational Intelligence Magazine.

[16]  D. Dudley,et al.  The immune system in health and disease. , 1992, Bailliere's clinical obstetrics and gynaecology.

[17]  A. Perelson,et al.  Predicting the size of the T-cell receptor and antibody combining region from consideration of efficient self-nonself discrimination. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Alan S. Perelson,et al.  Probability of Self-Nonself Discrimination , 1992 .

[19]  Claudia Eckert,et al.  On Permutation Masks in Hamming Negative Selection , 2006, ICARIS.

[20]  P. Helman,et al.  A formal framework for positive and negative detection schemes , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Fabio A. González,et al.  The Effect of Binary Matching Rules in Negative Selection , 2003, GECCO.

[22]  M. Eaman.  Immune system. , 2000, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[23]  Stephanie Forrest,et al.  Coverage and Generalization in an Artificial Immune System , 2002, GECCO.

[24]  G. Oster,et al.  Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self-non-self discrimination. , 1979, Journal of theoretical biology.

[25]  Jonathan Timmis,et al.  Artificial immune systems as a novel soft computing paradigm , 2003, Soft Comput..

[26]  Alex Alves Freitas,et al.  Revisiting the Foundations of Artificial Immune Systems: A Problem-Oriented Perspective , 2003, ICARIS.

[27]  Alan S. Perelson,et al.  Theoretical and Experimental Insights into Immunology , 1992, NATO ASI Series.

[28]  P. Delves,et al.  The immune system. First of two parts. , 2000, The New England journal of medicine.

[29]  Stephanie Forrest,et al.  An immunological model of distributed detection and its application to computer security , 1999 .