Network Security Prevention Model Based-Immune

Ya Ping Jiang; Shi Hui Cheng; Yong Gan

doi:10.4028/www.scientific.net/AMM.16-19.881

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 16-19Network Security Prevention Model Based-Immune

Network Security Prevention Model Based-Immune

Abstract:

With the concepts of self, nonself, antibody, vaccine and antigen in an intrusion detection and prevention system presented in this paper, the architecture of network intrusion and prevention based on immune principle is proposed. The intrusion information gotten from current monitored network is encapsulated and sent to the neighbor network as bacterin; therefore the neighbor network can make use of the bacterin and predict the danger of network. The experimental results show that the new model not only actualizes an active prevention method but also improves the ability of intrusion detection and prevention than that of the traditional passive intrusion prevention systems.

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Periodical:

Applied Mechanics and Materials (Volumes 16-19)

Pages:

881-885

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.16-19.881

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Online since:

October 2009

Authors:

Ya Ping Jiang, Shi Hui Cheng, Yong Gan

Keywords:

AIS, Matching, Network Security Prevention

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References

[1] J. Greensmith, U. Aickelin and J. Twycross: Proc. 5th International Conference on Artificial Immune Systems (Morgan-Kaufmann, San Francisco, June 5-7, 2006), Vol. 4163, pp.404-417.

Google Scholar

[2] S.A. Hofmeyr and S. Forrest: Proc. The Genetic and Evolutionary Computation Conference, (Morgan-Kaufmann, San Francisco, November 4-6, 1999), Vol. 2, pp.1289-1296.

Google Scholar

[3] T. Li: Computer Immunology (Publishing House of Electronics Industry, China, 2004), pp.1-232.

Google Scholar

[4] S. Forrest and A.S. Perelson: Proc. Symposium in Security and Privacy (Oakland, CA, July 3-5, 1994). Vol. 1, pp.202-213.

Google Scholar

[5] T. Li: Science in China Ser. F Information Sciences, Vol. 23 (2005) No. 3, pp.557-578. 0 20 40 60 80 100(s) 0 20 40 60 80 100(s) Packets/s(k) 30 20 10 0 1.

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2 0 concentration 1.

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2 0 concentration 1.

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2 0 concentration Fig. 3 The real attack intensity in the network Fig. 5 The antigen consistency vary curve in local host Fig. 4 The estimated attack intensity obtained by our method Fig. 6 The antigen consistency vary curve in neighbour host.

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