Positive and negative selection in a multilayer artificial immune system

The immune system is a complex and distributed system. It provides a multilayered form of defence, capable of identifying and responding to harmful pathogens that it does not recognise as “self”. The framework proposed in this paper incorporates a number of immunological concepts and principles, including the multilayered defence and the cooperation between cells in the adaptive immune system. An alternative model of positive selection is also presented. It is suggested that the framework discussed here could lead to reduced false positive responses in anomaly detection tasks, such as intrusion detection, as well being extended to a population of computational immune systems that are able to maintain population diversity of recognition and response.

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

[2]  Uwe Aickelin,et al.  Danger Theory: The Link between AIS and IDS? , 2003, ICARIS.

[3]  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.

[4]  Kristopher Kendall,et al.  A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems , 1999 .

[5]  Uwe Aickelin,et al.  The Danger Theory and Its Application to Artificial Immune Systems , 2008, ArXiv.

[6]  Jeffrey O. Kephart,et al.  A biologically inspired immune system for computers , 1994 .

[7]  Stephanie Forrest,et al.  Immunity by design: an artificial immune system , 1999 .

[8]  J. Playfair.  Infection and Immunity , 1995 .

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

[10]  Stephanie Forrest,et al.  Infect Recognize Destroy , 1996 .

[11]  M. Cohn.  Tritope model of restrictive recognition by the TCR. , 2003, Trends in immunology.

[12]  P. Matzinger.  Tolerance, danger, and the extended family. , 1994, Annual review of immunology.

[13]  Richard E. Overill,et al.  Design of an artificial immune system as a novel anomaly detector for combating financial fraud in the retail sector , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[14]  L. Sompayrac,et al.  How the immune system works , 1999 .

[15]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[16]  Zhou Ji,et al.  Artificial immune system (AIS) research in the last five years , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[17]  C. Janeway.  Immunobiology: The Immune System in Health and Disease , 1996 .

[18]  Peter J. Bentley,et al.  Negative selection and niching by an artificial immune system for network intrusion detection , 1999 .

[19]  Senhua Yu,et al.  MILA - Multilevel Immune Learning Algorithm , 2003, GECCO.

[20]  P. Matzinger.  The Danger Model: A Renewed Sense of Self , 2002, Science.

[21]  Kevin P. Anchor,et al.  CDIS: Towards a Computer Immune System for Detecting Network Intrusions , 2001, Recent Advances in Intrusion Detection.

[22]  Stephanie Forrest,et al.  Principles of a computer immune system , 1998, NSPW '97.

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

[24]  Stephanie Forrest,et al.  Automated response using system-call delays , 2000 .