International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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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)
  • Published : 2003.12.01
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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

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