虚假数据注入攻击对电力信息物理系统造成严重安全威胁。由于受到攻击样本与正常样本之间存在类别不平衡特性，导致机器学习检测方法偏向于多数类的预测，影响其对攻击的检测精度。为此，提出了基于Focal Loss^IM-Transformer的虚假数据注入攻击检测。Transformer利用其自注意力机制能够捕捉数据中的长期依赖性，进而识别不平衡的虚假数据注入攻击数据。Focal Loss^IM通过引入调制因子来更好地匹配虚假数据注入攻击样本的分布和特性，来增强检测方法对不平衡数据的识别能力，以提高检测方法对攻击的检测精度。通过在IEEE 14节点系统、IEEE 30节点系统和IEEE 57节点系统进行仿真，验证了所提方法的有效性。相较于传统损失函数和其他检测方法，所提方法显示出更好的泛化能力和对少数类的识别能力，且辨识精度高、误报率低。

False data injection attacks pose serious security threats to cyber-physical power system. Due to the class imbalance property between the attacked samples and the normal samples, machine learning detection methods tend to predict the majority of classes, which affects their detection accuracy of the attacks. Therefore, a false data injection attack detection based on Focal Loss^IM Transformer is proposed. Transformer utilizes itself attention mechanism to capture long-term dependencies in data, thereby identifies imbalanced false data injection attack data. Focal Loss^IM enhances the detection method's ability to identify imbalanced data by introducing modulation factors to better match the distribution and characteristics of false data injection attack samples, thereby improving the detection accuracy of the detection method for attacks.The effectiveness of the proposed method is verified through simulations on IEEE 14⁃node system, IEEE 30⁃node system, and IEEE 57⁃node system. Compared with traditional loss functions and other detection methods, the proposed method exhibits better generalization ability and recognition ability for minority classes, with high recognition accuracy and low false alarm rate.