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Immune Multi-agent Active Defense Model for Network Intrusion

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Book cover Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

Inspired by the immune theory and multi-agent systems, an immune multi-agent active defense model for network intrusion is established. The concept of immune agent is introduced. While its logical structure and running mechanism are established. The method which uses antibody concentration to quantitatively describe the degree of intrusion danger is presented. The proposed model implements a multi-layer and distributed active defense mechanism for network intrusion, and it is a new way to the network security.

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References

  1. Bai, Y., Kobayashi, H.: Intrusion Detection Systems: technology and development. IEEE Advanced Information Networking and Applications, pp. 710–715 (2003)

    Google Scholar 

  2. Li, T.: An immune based dynamic intrusion detection model. Chinese Science Bulletin 50, 2650–2657 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  3. Kim, J., Bentley, P.: The Artificial Immune Model for Network Intrusion Detection. In: 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT 1999), Aachen, Germany (1999)

    Google Scholar 

  4. Li, T.: An immunity based network security risk estimation. Science in China Ser. F Information Sciences 48, 557–578 (2005)

    Article  MATH  Google Scholar 

  5. Hegazy, I.M., Faheem, H.M., Al-Arif, T., Ahmed, T.: Evaluating how well agent-based IDS perform. Potentials, Digital Object Identifier, IEEE 24, 27–30 (2005)

    Google Scholar 

  6. Shi, Z.Z.: Intelligent agent and their application [M]. Science Press, Beijing (2000)

    Google Scholar 

  7. Jerne, N.K.: Towards a Network Theory of the Immune System. Annnual Immunology 125c (1974)

    Google Scholar 

  8. Li, T.: Computer Immunology. Publishing House of Electronics Industry, Beijing (2004)

    Google Scholar 

  9. Forrest, S., Perelson, A.S.: Self-Nonself Discrimination in a Computer. In: Proceedings of IEEE Symposium on Research in Security and Privacy, Oakland (1994)

    Google Scholar 

  10. Dasgupta, D.: An Artificial Immune System as a Multi-Agent Decision Support System. In: Proc. of the IEEE International Conference on SMC, San Diego (1998)

    Google Scholar 

  11. Farmer, J.D., Packard, N.H., Perelson, A.S.: The Immune System, Adaption, and Machine Learning, vol. 22d. Physica (1986)

    Google Scholar 

  12. Ayara, T.: Negative Selection: How to Generate Detectors. In: Proc. of 1st International Conference on Artificial Immune Systems, University of Kent Canterbury (2002)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Liu, S. et al. (2006). Immune Multi-agent Active Defense Model for Network Intrusion. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_14

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  • DOI: https://doi.org/10.1007/11903697_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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