Hybrid Statistical Learning Model for Intrusion Detection of Networks


The KIPS Transactions:PartC, Vol. 10, No. 6, pp. 705-710, Oct. 2003
10.3745/KIPSTC.2003.10.6.705,   PDF Download:

Abstract

Recently, most interchanges of information have been performed in the internet environments. So, the technique, which is used as intrusion detecting tool for system protecting against attack, is very important. But, the skills of intrusion detection are newer and more delicate, we need preparations for defending from these attacks. Currently, lots of intrusion detection systems make the model of intrusion detection rule using experienced data, based on this model they have the strategy of defence against attacks. This is not efficient for defense from new attack. In this paper, a new model of intrusion detection is proposed. This is hybrid statistical learning model using likelihood ratio test and statistical learning theory, then this model can detect a new attack as well as experienced attacks. This strategy performs intrusion detection according to make a model by finding abnormal attacks. Using KDD Cup-99 task data, we can know that the proposed model has a good result of intrusion detection.


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Cite this article
[IEEE Style]
J. S. Hae, "Hybrid Statistical Learning Model for Intrusion Detection of Networks," The KIPS Transactions:PartC, vol. 10, no. 6, pp. 705-710, 2003. DOI: 10.3745/KIPSTC.2003.10.6.705.

[ACM Style]
Jeon Seong Hae. 2003. Hybrid Statistical Learning Model for Intrusion Detection of Networks. The KIPS Transactions:PartC, 10, 6, (2003), 705-710. DOI: 10.3745/KIPSTC.2003.10.6.705.