Today, due to the growing use of computer networks, the issue of security of these networks and the use of intrusion detection systems has received serious attention. A major challenge in intrusion detection systems is the enormous amount of data. The generalization capability of support vector machine has attracted the attention of intrusion detection systems in the last years. The main drawbacks of a support vector machine occur during its training phase, which is computationally expensive and dependent on the size of the input dataset. In this study, a new algorithm to speed up support vector machine training time is presented. In proposed method, First, Ant Colony Optimization (ACO) is used to find prototype samples, then a number of prototype samples is randomly selected and the approximate boundary is determined using support vector machine. Based on the approximate boundary obtained, boundary samples are determined using decision tree. Using these boundary samples, final model is obtained. To demonstrate the effectiveness of the proposed method, standard publicly available datasets have been used. The experiment results show that despite the data reduction, the proposed model produces results with similar accuracy and in a faster way than state-of-the art and the current Support Vector Machine (SVM) implementations.

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