This paper "an ensemble of Support Vector Machines (SVM)" for networkbased intrusion detection. Bootstrapping is applied to derive various training sets from the given training set. Then a SVM is derived for each training set. The decisions of all SVMs is taken and majority voting is considered to classify the given query pattern as a normal or an anomalous one. We have shown the results of applying an ensemble of Support Vector Machines to the two standard data sets,viz.,1999KDDCupandCreditcarddatasets.
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