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Radial basis function (RBF) has been one of the most common neural networks used in the intrusion detection system (IDS). To improve the approximation performance and calculation speed of RBF, we describe a method to deal with the benchmark datasets adopted in the research. It includes converting the string to numeric elements firstly, then omitting the unnecessary data and ensuring that the data has the reasonable range limit. The simulation results built upon Matlab software show that the RBF neural network has better performance than BP neural network.