### FDIA Detection Based on a Data-Driven Approach  
However, model-based methods for detecting FDIAs have certain limitations, such as the tendency of local convergence during online identification and the difficulty in selecting model thresholds. With the development of CPS, the model-based approach is insufficient to cope with the state estimation problem created by the increasing quantity of data. Therefore, a detection method that integrates data-driven, method-based, and intelligent algorithms has emerged. Reference [15] proposes an online FDIA detection method combining wavelet transform and deep neural networks to detect spatial and temporal data inconsistencies caused when spurious data are injected into state vectors. A superior ability to handle multi-dimensional data features is demonstrated; however, in certain cases, the fine-tuning of model hyperparameters is imperative to attain optimal performance. Reference [16] proposes an FDIA detection method based on the Kalman filter algorithm and recurrent neural networks (RNNs), which obtains dynamic thresholds based on the fit of observations and predictions to determine whether FDIAs occur. To ensure that FDIAs do not affect the accuracy of state estimation and further improve the accuracy of FDIA detection, effective detection methods need to be developed. The adaptability of detection is bolstered through the utilization of dynamic thresholds that can adjust to changing scenarios. Concurrently, stringent demands are placed on system modeling and parameter adjustment, necessitating real-time threshold updates. A framework for fault identification and diagnosis is introduced in [17]. It harnesses graph-edge conditional convolutional networks. The fundamental objective underlying this framework pertains to the establishment of a mapping relationship, one that aligns measurement estimates with the authentic state of the power system. The consideration of power system topology renders the method suitable for intricate systems. However, the construction of graph convolutional networks requires a substantial volume of training data, resulting in heightened computational complexity. In [18], an online intelligent anomaly and attack detection method is introduced, which makes use of the partially observable Markov decision process. This method is designed to detect cyber-attacks targeting smart grids. The method takes into account the dynamics and incomplete observability inherent to the system, rendering it applicable to complex network environments. Nevertheless, it necessitates high precision in system model accuracy and exhibits sensitivity to model errors. A distributed microgrid control system FDI attack detection structure is presented in [19], which is based on the utilization of a Gaussian process regression and a one-class support vector machine anomaly detection algorithm. Due to its suitability for distributed microgrid control systems characterized by strong distribution and real-time performance, the method may require specialized knowledge and time investment for model parameter selection and adjustment, contributing to heightened implementation complexity. The evaluation of the introduced attack detector encompasses two viewpoints: the assessment of detection loss probability and false alarm probability. The data-driven approach introduced in [20] demonstrates the integration of residual networks and attention-mechanism-based long short-term memory models. This integration is directed toward the achievement of temporal correlation and feature extraction in measurement data. Through the amalgamation of these models, the temporal dependencies inherent in the data are efficiently captured. It should be noted that the practical application of this method might encounter challenges, including the intricacy associated with model fusion and the need for parameter tuning. However, a single deep neural network is susceptible to adversarial attacks, which may lead to poor output stability in the trained network.