### FDIA Detection Based on a Data-Driven Approach
Accordingly, rooted in the aforementioned concepts of various FDIA detection methodologies, the focus of this study is directed toward the exploration of the practical implementation of FDIA presence within power system state estimation. In operational scenarios, historical data in terms of practical application may involve instances of sample imbalance due to the scarcity of attack data. This scarcity can subsequently engender noteworthy discrepancies within data-driven detection models, resulting in substantial errors. Owing to the intricate nature of acquiring comprehensive CPPS information, the proposed model in this paper is developed from the perspective of attackers. Consequently, a multi-attack model involving the injection of false data is formulated that is contingent upon the extent of information they possess concerning the network structure. This endeavor aims to establish a closer alignment with real-world application scenarios. Building upon this foundational premise, an FDIA detection method is proposed in this paper, wherein the amalgamation of the GAN-GRU data balanced processing model and the Transformer neural network is employed. This confluence is purposefully designed to effectively address the challenge posed by the diminished attack detection rate, arising from both data imbalance and intricate attack patterns. The contribution of this paper is as follows:  
1. An attacker-centric perspective is embraced in this study to comprehensively investigate the system's security, with an intention to think and analyze from the viewpoint of potential attackers. Thus, within this investigation, diverse tiers of information are taken into account, influencing the creation of a spectrum of attack patterns. These patterns encompass distinct quantities of attack buses with varying numbers and intensities. Through the manipulation of both the count and strength of attack buses, an array of plausible attack scenarios can be simulated, facilitating a holistic comprehension of the ramifications these attacks might impose upon the day-to-day operation of the system. 2. To address the challenge posed by the imbalance in historical measurement data in practical scenarios, a data-balancing processing model based on GAN-GRU is introduced in this study. Within this model, a GRU neural network is incorporated into the GAN framework, serving as integral components of the generator and discriminator, respectively. Through jointly training both the GAN network and the GRU network, a recurrent network is constructed with the purpose of generating a limited segment of attack vector data within an imbalanced sample set. This process facilitates the establishment of balance among the quantities of diverse sample classes in the training dataset, ultimately leading to enhanced accuracy and reliability of the models when subjected to FDIA assessment. 3. In response to the challenge posed by false data injection across various attack scenarios, a false data detection model is constructed utilizing the Transformer neural network. The equilibrium dataset is divided into distinct training and test subsets, enabling the application of the Transformer to the detection and classification of false data injection attacks. Following this, a confusion matrix is generated, contrasting projected and actual values, and comparison experiments are performed against the enhanced convolutional neural network-gated recurrent unit (CNN-GRU), long short-term memory network (LSTM), and support vector machine (SVM). The simulation findings underscore the commendable scalability of the proposed data-balancing processing model based on GAN-GRU and the detection algorithm based on the Transformer neural network when scrutinizing the FDIA within the IEEE 118-bus system. Furthermore, the outcomes demonstrate a substantial enhancement over conventional algorithms in terms of precision in detecting attacks.