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Research Article
 

An Novel Intrusion Detection System Based on Naive Bayesian Algorithm



Hui Wang, Hongyu Chen and Shanshan Yang
 
ABSTRACT

With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network Intrusion Detection System (IDS). According to the deficiency of the Naive Bayesian (NB) algorithm, this paper presents an improved NB algorithm, which is addition of an attribute-added method to the traditional NB. This algorithm which is based on the original model and combined with a controlling parameter to enhance the accuracy of classification, the best parameter obtained by experiments can not only simplify both the time complexity and space complexity of the intrusion detection but also optimize the classification performance. The experimental results prove that this proposed approach applied into the intrusion detection framework can drastically reduce the false alarm rate of IDS so as to improve the detection efficiency and decrease economic damage brought by the cyber attack.

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  How to cite this article:

Hui Wang, Hongyu Chen and Shanshan Yang, 2013. An Novel Intrusion Detection System Based on Naive Bayesian Algorithm. Journal of Applied Sciences, 13: 1865-1870.

DOI: 10.3923/jas.2013.1865.1870

URL: https://scialert.net/abstract/?doi=jas.2013.1865.1870

REFERENCES
Farid, D.M., N. Harbi and M.Z. Rahman, 2010. Combining naive bayes and decision tree for adaptive intrusion detection. Int. J. Network Secur. Appl., 2: 12-25.
CrossRef  |  

Jiang, L., Z. Cai, D. Wang and H. Zhang, 2012. Improving tree augmented naive bayes for class probability estimation. Knowl. Based Syst., 26: 239-245.
CrossRef  |  Direct Link  |  

Mohammad, M.N., N. Sulaiman and O.A. Muhsin, 2011. A novel intrusion detection system by using intelligent data mining in weka environment. Procedia Comput. Sci., 3: 1237-1242.
CrossRef  |  

Panda, M. and M.R. Patra, 2007. Network intrusion detection using naive bayes. Int. J. Comput. Sci. Network Secur., 7: 258-263.
Direct Link  |  

Sindhu, S.S.S., S. Geetha and A. Kannan, 2012. Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst. Applic., 39: 129-141.
CrossRef  |  Direct Link  |  

Wang, G., J. Hao, J. Ma and L. Huang, 2010. A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Applic., 37: 6225-6232.
CrossRef  |  Direct Link  |  

Yang, J., Y. Liu, Z. Liu, X. Zhu and X. Zhang, 2011. A new feature selection algorithm based on binomial hypothesis testing for spam filtering. Knowl. Based Syst., 24: 904-914.
CrossRef  |  

Yi, Y., J. Wu and W. Xu, 2011. Incremental SVM based on reserved set for network intrusion detection. Expert Syst. Appl., 38: 7698-7707.
CrossRef  |  

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