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http://dx.doi.org/10.3745/KIPSTC.2003.10C.6.705

Hybrid Statistical Learning Model for Intrusion Detection of Networks  

Jun, Sung-Hae (청주대학교 통계학과)
Publication Information
The KIPS Transactions:PartC / v.10C, no.6, 2003 , pp. 705-710 More about this Journal
Abstract
Recently, most interchanges of information have been performed in the internet environments. So, the technuque, which is used as intrusion deleting 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 systemsmake the midel 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 abnomal attacks. Using KDD Cup-99 task data, we can know that the proposed model has a good result of intrusion detection.
Keywords
Intrusion Detection; Statistical Learning Model; Likelihood Ratio Testing;
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Times Cited By KSCI : 3  (Citation Analysis)
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