In order to solve the problem of low detection accuracy of class imbalance data by existing intrusion detection systems based on machine learning, an intrusion detection method based on conditional Wasserstein generative adversarial network (CWGAN) and deep neural network (DNN) is proposed. CWGANDNN improve the class imbalance problem of data sets by generating samples, and the detection efficiency of intrusion detection system (IDS) on minority and unknown classes is increased. Firstly, the data are preprocessed by the variation Gaussian mixture model (VGM) to decompose the mixed distribution of continuous features. And then the CWGAN is used to learn the distribution of original dataset and generate minorityclass data to balance the training dataset, and train the DNN with balanced dataset. Finally, the trained DNN is used for intrusion detection. The experimental results on NSLKDD dataset show that the data generated by CWGAN can improve DNN’s classification accuracy and F1 score by 5%, but AUC decreases by 2%. Compared with other equalization methods, the accuracy, F1 score and AUC of CWGANDNN are improved by at least 3%, 1% and 1%.