Conserved Self Pattern Recognition Algorithm

Authors:
Senhua Yu;Dipankar Dasgupta
Affiliations:
Department of Computer Science, University of Memphis, Memphis, USA TN 38152;Department of Computer Science, University of Memphis, Memphis, USA TN 38152
Venue:
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Year:
2008

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Abstract

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).