The Hybrid Model using SVM and Decision Tree for Intrusion Detection 


Vol. 14,  No. 1, pp. 1-6, Feb.  2007
10.3745/KIPSTC.2007.14.1.1


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  Abstract

In order to operate a secure network, it is very important for the network to raise positive detection as well as lower negative detection for reducing the damage from network intrusion. By using SVM on the intrusion detection field, we expect to improve real-time detection of intrusion data. However, due to classification based on calculating values after having expressed input data in vector space by SVM, continuous data type can not be used as any input data. Therefore, we present the hybrid model between SVM and decision tree method to make up for the weak point. Accordingly, we see that intrusion detection rate, F-P error rate, F-N error rate are improved as 5.6%, 0.16%, 0.82%, respectively.

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[IEEE Style]

N. K. Um, S. H. Woo, S. H. Lee, "The Hybrid Model using SVM and Decision Tree for Intrusion Detection," The KIPS Transactions:PartC, vol. 14, no. 1, pp. 1-6, 2007. DOI: 10.3745/KIPSTC.2007.14.1.1.

[ACM Style]

Nam Kyoung Um, Sung Hee Woo, and Sang Ho Lee. 2007. The Hybrid Model using SVM and Decision Tree for Intrusion Detection. The KIPS Transactions:PartC, 14, 1, (2007), 1-6. DOI: 10.3745/KIPSTC.2007.14.1.1.

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