Abstract. Design and implementation of intrusion detection systems remain an important research issue in order to maintain proper network security. Support Vector Machines (SVM) as a classical pattern recognition tool have been widely used for intrusion detection. However, conventional SVM methods do not concern different characteristics of features in building an intrusion detection system. We propose an enhanced SVM model with a weighted kernel function based on features of the training data for intrusion detection. Rough set theory is adopted to perform a feature ranking and selection task of the new model. We evaluate the new model with the KDD dataset and the UNM dataset. It is suggested that the proposed model outperformed the conventional SVM in precision, computation time, and false negative rate.

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