The Application of Network Intrusion Detection Technology in Instrument

Luo Guang Huang; Li Min Meng; Yong Hong Guo

doi:10.4028/www.scientific.net/AMM.325-326.1683

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 325-326The Application of Network Intrusion Detection...

The Application of Network Intrusion Detection Technology in Instrument

Abstract:

The development of intrusion detection systems in the world are reviewed in this article first. On the basis of in-depth analysis of the characteristics of network attacks and intrusions we aim at to solving the problems mentioned above, the characteristics of survival of the fittest genetic algorithm is used to solve the problem. Second, a detection model based on genetic algorithms is established, and finally the model is simulated. The simulation results show that the model can solve its intrusion detection system, security issues, with a theoretical and practical application.

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Periodical:

Applied Mechanics and Materials (Volumes 325-326)

Pages:

1683-1687

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.325-326.1683

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Online since:

June 2013

Authors:

Luo Guang Huang, Li Min Meng, Yong Hong Guo

Keywords:

Genetic Algorithm (GA), Intrusion Detection, Network Security

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References

[1] Booker G. A fast method for real-time anomaly detection using routing statistics. In:2008 IEEE INFOCOM workshops 2008. Phoneix, AZ, USA. 2008. Piscataway, NJ, USA:IEEE, 2008.1~3

DOI: 10.1109/infocom.2008.4544604

Google Scholar

[2] Zhou L, Schneider F, Robert R. APSS: Proactive secret sharing in Asynchronous systems. ACM Transactions on Information and System Security, 2005, 8(3):259~286

DOI: 10.1145/1085126.1085127

Google Scholar

[3] M Han, Y Wang. Analysis and modeling of multivariate chaotic time series based on neural network. Expert Systems with Applications, 2009, 36(2):1280~1290

DOI: 10.1016/j.eswa.2007.11.057

Google Scholar

[4] Matthias B, Thomas D, Bernhard P. Enhanced Internet security by a distributed traffic control service based on traffic ownership. Network and Computer Applications, 2007, 30:841~857.

DOI: 10.1016/j.jnca.2005.07.006

Google Scholar

[5] Yong Zheng Zhang, Bin Xing Fang, Xiao Chun Yun. A risk assessment approach for network information system. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004. 26~29.

DOI: 10.1109/icmlc.2004.1378537

Google Scholar

[6] Mukhe B., Heberlein L. T, Levitt K. N. Network intrusion detection [J]. IEEE netork, 1994, 8(3): 26~41.

Google Scholar

[7] M. Cai, K. Hwang,Y. K. Kwok, S. Song, Y. Chen. Collaborative Internet Worm Containment. IEEE Security and privacy Magazine. 2005, 11(5):25~33

DOI: 10.1109/msp.2005.63

Google Scholar

[8] Manikopoulos C., Papavassiliou S. Network intrusion and fault detection: A statistical Anomaly approach. IEEE Communications Magazine, 2002, 40(10):76~52

DOI: 10.1109/mcom.2002.1039860

Google Scholar