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A new kind of Intrusion Detection System (IDS) based on Principal Component Analysis (PCA) and Grey Neural Networks (GNN) is presented to improve the performance of BP neural networks in the field of intrusion detection. First, the pre-processed data set is normalized and the features of them are extracted by PCA. Next, five layers of the grey neural networks is designed based on BP neural networks and Grey theory, then the IDS composed of sniffer module, data processing module, grey neural network module and intrusion detection module is presented. Finally, the presented system was tested on the data set of DARPA 1999. The results demonstrate that the feature extraction reduced the dimensionality of feature space greatly without degrading the systems' performance, and GNN not only promote the parallel computing power of the system but also improve the utilization of available information.