In response to the problems of decreased robustness and insufficient feature expression ability caused by noise and outlier interference in traffic anomaly detection models, and low minority class detection rates when dealing with imbalanced high-dimensional massive data, a traffic anomaly detection method based on generative adversarial networks was proposed. Firstly, the clustering based on SCiForest algorithm is used to detect outliers and reduce their impact on the subsequent training of the generative adversarial network. Secondly, a denoising autoencoder-based generative adversarial network (DGAN) is designed to generate reliable synthetic minority class samples. The network defines its training target based on the Wasserstein distance between reconstructed error distributions, effectively alleviating the problem of data imbalance. Again, using a denoising autoencoder (DAE) with the same architecture as the generative adversarial network discriminator, real and synthetic samples are input for reconstruction training, and the optimized encoder part is extracted as the feature extraction and dimensionality reduction module to enhance feature expression ability. Finally, the processed data is input into the feature fusion model of CNN and BiGRU (CNN-BiGRU-FFusion) model, which completes classification and detection based on capturing spatial and temporal features. The accuracy and F1 score on the NSL-KDD dataset reached 92.06% and 92.25%, respectively, verifying the superior performance of the proposed method in network traffic anomaly detection tasks. The feasibility of the method was further validated through experiments on the CICIDS2017 dataset.