Modeling of positive selection for the development of a computer immune system and a self-recognition algorithmModeling of Positive Selection for the Development of a Computer Immune System and a Self-Recognition Algorithm
- Authors
- Sim, K.-B.; Lee, D.-W.
- Issue Date
- Dec-2003
- Publisher
- 제어·로봇·시스템학회
- Keywords
- Immune system; MHC set; Negative selection; Positive selection; Self-recognition algorithm
- Citation
- International Journal of Control, Automation and Systems, v.1, no.4, pp 453 - 458
- Pages
- 6
- Journal Title
- International Journal of Control, Automation and Systems
- Volume
- 1
- Number
- 4
- Start Page
- 453
- End Page
- 458
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/26258
- ISSN
- 1598-6446
2005-4092
- 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.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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