Advertisement

Immune Multi-agent Active Defense Model for Network Intrusion

  • SunJun Liu
  • Tao Li
  • DianGang Wang
  • Kui Zhao
  • Xun Gong
  • XiaoQing Hu
  • Chun Xu
  • Gang Liang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

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.

Keywords

Intrusion Detection Intrusion Detection System Binary String Network Security Artificial Immune System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bai, Y., Kobayashi, H.: Intrusion Detection Systems: technology and development. IEEE Advanced Information Networking and Applications, pp. 710–715 (2003)Google Scholar
  2. 2.
    Li, T.: An immune based dynamic intrusion detection model. Chinese Science Bulletin 50, 2650–2657 (2005)MATHCrossRefMathSciNetGoogle Scholar
  3. 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. 4.
    Li, T.: An immunity based network security risk estimation. Science in China Ser. F Information Sciences 48, 557–578 (2005)MATHCrossRefGoogle Scholar
  5. 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. 6.
    Shi, Z.Z.: Intelligent agent and their application [M]. Science Press, Beijing (2000)Google Scholar
  7. 7.
    Jerne, N.K.: Towards a Network Theory of the Immune System. Annnual Immunology 125c (1974)Google Scholar
  8. 8.
    Li, T.: Computer Immunology. Publishing House of Electronics Industry, Beijing (2004)Google Scholar
  9. 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. 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. 11.
    Farmer, J.D., Packard, N.H., Perelson, A.S.: The Immune System, Adaption, and Machine Learning, vol. 22d. Physica (1986)Google Scholar
  12. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • SunJun Liu
    • 1
  • Tao Li
    • 1
  • DianGang Wang
    • 1
  • Kui Zhao
    • 1
  • Xun Gong
    • 1
  • XiaoQing Hu
    • 1
  • Chun Xu
    • 1
  • Gang Liang
    • 1
  1. 1.School of Computer ScienceSichuan Univ.ChengduChina

Personalised recommendations