UNITED STATES
Intrusion Detection System Using Support Vector Machine and Decision Tree
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10.5120/758-993 |
Snehal A Mulay, P R Devale and G V Garje. Article:Intrusion Detection System Using Support Vector Machine and Decision Tree. International Journal of Computer Applications 3(3):40–43, June 2010. Published By Foundation of Computer Science. BibTeX
@article{key:article,
author = {Snehal A. Mulay and P.R. Devale and G.V. Garje},
title = {Article:Intrusion Detection System Using Support Vector Machine and Decision Tree},
journal = {International Journal of Computer Applications},
year = {2010},
volume = {3},
number = {3},
pages = {40--43},
month = {June},
note = {Published By Foundation of Computer Science}
}
Abstract
Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems. This method can decrease the training and testing time, increasing the efficiency of the system. The different ways to construct the binary trees divides the data set into two subsets from root to the leaf until every subset consists of only one class. The construction order of binary tree has great influence on the classification performance. In this paper we are studying an algorithm, Tree structured multiclass SVM, which has been used for classifying data. This paper proposes the decision tree based algorithm to construct multiclass intrusion detection system.
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