Network Intrusion Detection Model Based on Genetic Algorithm Optimizing Parameters of Support Vector Machine

Chun Liu

doi:10.4028/www.scientific.net/AMR.989-994.2012

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Network Intrusion Detection Model Based on Genetic...

Network Intrusion Detection Model Based on Genetic Algorithm Optimizing Parameters of Support Vector Machine

Abstract:

Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Parameters selection of support vector machine is a important problems in network intrusion detection. In order to improve network intrusion detection precision, this paper proposed a network intrusion detection model based on parameters of support vector machine (SVM) by genetic algorithm. The performance of the model was tested by KDD Cup 99 data. Compared with other network intrusion detection models, the proposed model has significantly improved the detection precision of network intrusion.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

2012-2015

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.2012

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Online since:

July 2014

Authors:

Chun Liu*

Keywords:

Genetic Algorithm (GA), Model Parameters, Network Intrusion Detection, Support Vector Machine (SVM)

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* - Corresponding Author

References

[1] Panda M, Patra MR. Bayesian. Belief network with genetic local search for detecting network intrusions. International journal of secure digital information age 2009; 1(1): 34-44.

Google Scholar

[2] Khan L., Awad M., Thuraisingham B. A new intrusion detection system using support vector machines and hierarchical clustering. The VLDB Journal, 2007, 16(2):. 507-521.

DOI: 10.1007/s00778-006-0002-5

Google Scholar

[3] Zhang CL, Jiang J, Karnel M. Comparison of back propagation learning and radial basis function network in Intrusion detection system. In: Rough set, fuzzy set, data mining and granular computing, 2003; 2639: 466-70.

DOI: 10.1007/3-540-39205-x_79

Google Scholar

[4] Tavallaee M, Stakhanova N, Ghorbani A. Towards credible evaluation of anomaly based intrusion detection methods. IEEE Transaction on System, Man and Cybernetics. Part-c, Applications and Reviews, 2010; 40(5): 516-24.

DOI: 10.1109/tsmcc.2010.2048428

Google Scholar

[5] Natesan P., Balasubramanie P., Gowrison G. Improving attack detection rate in network intrusion detection using adaboost algorithm with multiple weak classifiers. Journal of Information and Computational Science, 2012, 8(8): 2239-2251.

DOI: 10.4018/978-1-4666-8761-5.ch011

Google Scholar