Decision tree based Support Vector Machine for Intrusion Detection

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 in Intrusion Detection Systems (IDS). This method can decrease the training and testing time of the IDS, 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 two decision tree approaches: Hierarchical multiclass SVM and Tree structured multiclass SVM, to construct multiclass intrusion detection system.