Conserved Self Pattern Recognition Algorithm

Self-nonself model makes a lot of sense in the mechanisms of self versus nonself recognition in the immune system but it failed to explain a great number of findings. Some new immune theory is proposed to accommodate incompatible new findings, including Pattern Recognition Receptors (PRRs) Model and Danger Theory. Inspired from the PRRs model, a novel approach called Conserved Self Pattern Recognition Algorithm (CSPRA) is proposed in this paper. The algorithm is tested using the famous benchmark Fisher's Iris data. Preliminary results demonstrate that the new approach lowers the false positive and thus enhances the efficiency and reliability for anomaly detection without increase in complexity comparing to the classical Negative Selection Algorithm (NSA).

[1]  Fabio A. González,et al.  An immuno-fuzzy approach to anomaly detection , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[2]  Julie Greensmith,et al.  Immune System Approaches to Intrusion Detection - A Review , 2004, ICARIS.

[3]  Riccardo Poli,et al.  Genetic and Evolutionary Computation – GECCO 2004 , 2004, Lecture Notes in Computer Science.

[4]  D E Koshland,et al.  Recognizing self from nonself. , 1990, Science.

[5]  Zhou Ji,et al.  Real-Valued Negative Selection Algorithm with Variable-Sized Detectors , 2004, GECCO.

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

[7]  Nadia Nedjah,et al.  Computational Intelligence in Information Assurance and Security , 2007, Studies in Computational Intelligence.

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

[9]  Charles A. Janeway,et al.  Decoding the Patterns of Self and Nonself by the Innate Immune System , 2002, Science.

[10]  Ki-Won Yeom,et al.  Immune-inspired Algorithm for Anomaly Detection , 2007, Computational Intelligence in Information Assurance and Security.

[11]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

[12]  Simon M. Garrett,et al.  How Do We Evaluate Artificial Immune Systems? , 2005, Evolutionary Computation.

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

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

[15]  C. Janeway.  Approaching the asymptote? Evolution and revolution in immunology. , 1989, Cold Spring Harbor symposia on quantitative biology.

[16]  C. Janeway,et al.  The immune system evolved to discriminate infectious nonself from noninfectious self. , 1992, Immunology today.

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