Most of the existing abnormal traffic methods are based on supervised learning. It is extremely difficult to obtain and mark abnormal traffic data samples in real life， and there are many limitations. In addition， due to the diversity and complexity of abnormal network data， the adaptability of various detection methods is poor， and it is difficult to judge the new abnormal traffic. Based on the above problems， this paper designs a semi-supervised abnormal flow detection framework， MeAEG-Net （Memory Augment Based on Generative Adversarial Network）， to detect anomalies by training only normal flow sample data and comparing the reconstruction errors of the underlying characteristics of input flow of generator module. A generative adversarial network is used in the model to better train the generator. The generator adopts the structure of an autoencoder and decoder to solve the problem that the autoencoder is susceptible to noise. The memory-augmented module is added to the sub-network of the autoencoder to weaken the generalization ability of the generator module and increase the reconstruction error of abnormal traffic. Experimental results show that the method proposed in this paper can achieve a good effect on abnormal traffic detection under the premise of learning only normal traffic data samples. Finally， the future research direction and challenges have been prospected.