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Modeling of Positive Selection for the Development of a Computer Immune System and a Self-Recognition Algorithm  

Sim, Kwee-Bo (School of Electrical and Electronic Engineering, Chung-Ang University)
Lee, Dong-Wook (Information and Telecommunication Research Institute, Chung-Ang University)
Publication Information
International Journal of Control, Automation, and Systems / v.1, no.4, 2003 , pp. 453-458 More about this Journal
Abstract
The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.
Keywords
Immune system; MHC set; negative selection; positive selection; self-recognition algorithm;
Citations & Related Records

Times Cited By SCOPUS : 4
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