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Self-nonself discrimination in a computer
Abstract
The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. The authors describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses.>
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Research works printed but not linked: 12
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- 10.1162/evco.1993.1.3.191 · DOI
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- hightower, 0, The evolution of secondary organization in immune system gene libraries, Proceedings of the Second European Conference on Artificial Life
- uspensky, 1937, Introduction to Mathematical Probability, 77
- 10.1073/pnas.90.5.1691 · DOI
- ludwig, 1991, The Little Black Book of Computer Viruses
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Metadata
- Authors
- Forrest, S., Perelson, A. S., Allen, L., & Cherukuri, R.
- Venue
- Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-12-17
- Pages
- 202-212
- OpenAlex
- W2129624205
- Cited by
- 1,332
- References
- 0
- FWCI
- 78.38
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Self-nonself discrimination in a computer. https://app.cochat.ai/papers/10.1109/risp.1994.296580
- The paper
- Forrest, S., Perelson, A. S., Allen, L., & Cherukuri, R. (2002). Self-nonself discrimination in a computer. Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy. https://doi.org/10.1109/risp.1994.296580
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
