A New Research on Instrusion Detection System Based on Artificial Immune

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Abstract:

The paper proposed a new model by applying biological immune into intrusion detection system, in this new model, generated algorithm of the mature detection get improved, the self-et realized dynamic, co-evolution module can effectively find the system potential vulnerabilities and generate the corresponding patch to strengthen the system. As of result, simulation experiment for this new model is did, through the analysis of the result for simulation experiment, it shows that the new model and method has higher rate in making matured detector than the traditional model and method, and new model also has higher detecting rate on intrusion detection. To sum up, the co-evolution method is able to strengthen the system effectively.

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2728-2731

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] United states general accounting office. Information security: computer attacks at department of defense pose increasing risks[J]. GAO/AIMD-96-84, USA, (1996).

Google Scholar

[2] CERT Coordination Center [EB/OL], http: /www. cert. org/encyc_article/tocencyc. html.

Google Scholar

[3] T. Verwoerd, R. Hunt. Intrusion detection techniques and approaches [J], Computer Communications, 2002, 25: 1356-1365.

DOI: 10.1016/s0140-3664(02)00037-3

Google Scholar

[4] K. L. Fox, R. R. Ilenning, J. H. Reed, R. Simonian. An Neural Network Approach Towards Intrusion detection[A], In Proceedings of the 13th National Computer Security Conference[C], (1990).

Google Scholar

[5] W. Lee, S. J. Stolof, K. W. Mok. A Data Mining Framework for Building Intrusion Detection Models [J],IEEE Symposium on Security and Privacy, (1999).

DOI: 10.1109/secpri.1999.766909

Google Scholar

[6] Goldberg D, Deb. K. A Comparative Analysis of Selection Schemes Used in Genetic Algorithms[J], Foundations of Genetic Algorithms, 1991: 69-93.

DOI: 10.1016/b978-0-08-050684-5.50008-2

Google Scholar

[7] L. J. Eshelman, J. D. Schaffer. Real-Coded Genetic Algorithms and Interval Schemata[J], Foundations of Genetic Algorithms, 1993, 2: 187-202.

DOI: 10.1016/b978-0-08-094832-4.50018-0

Google Scholar