Abstract: With the development of new-type energy internet, large-scale sensing measurement systems provide data support for data-driven detection of false data injection attack. However, the problem of unbalanced attack data will affect the performance of such methods. Therefore, a data rebalance attack detection model based on improved generative adversarial network（GAN） and extremely randomized tree is proposed. Firstly, the GAN structure is designed to make the training procedure stable enough to generate high-quality data. Secondly, the Copula function is used to construct the spatial correlation between the power system states to adapt to the integration of the distributed energy resources. Then, a rebalanced dataset is obtained through the adversarial training of the improved GAN, and the extremely randomized tree classifier is used to detect the attack. In addition, the data validity index based on multiple classifiers is designed to evaluate the quality of the generated data. The effect of the proposed method is verified by comparative experiments. Results show that the method can generate high-quality measurement data, solve the problem of data imbalance, and the attack detection rate is 98.95%.