## 1 Introduction
The idea of \"first difference\" from statistics and economics inspired the authors of this study to develop a classifier that can identify dangers to network time synchronization. They found that Artificial Neural Networks outperformed traditional techniques for detecting network security issues. An ANN model was used to identify MITM assaults, and the authors noted a high detection rate. The authors of [20] used machine learning techniques to identify and remove hackers from smart grids. The simulations conducted for this study showed that the suggested approach might have a high detection rate. Deep learning has also been used to track cyberattacks on the smart grid. For example, the authors of [21] created a deep neural network and a deep learning ensemble technique based on decision trees. Ten-fold cross-validation was employed to assess the model. The evaluation results show that the suggested model beats the most effective methods currently available, such as random forest, Ada Boost, and DNN [22]. Cyberattacks on the smart grid can potentially be discovered through data mining, a type of AI. The authors of [20] discussed past research that used data mining techniques to spot fake data injection attacks in smart grids. These methods allow you to explore data patterns that you usually wouldn't be able to see and find ways in vast amounts of data. In [23], the authors used the data mining method known as Common Path Mining to find FDIA in their networks. To describe how the samples were arranged, they chose to use the idea of a \"route.\" Every unique incident has a different course that has a wide range of flaws. A sequence is considered an attack if it fits within one of the paths. A Casual Event Graph can be used by the authors of [24] to identify FDIA in smart grids. The training of historical datasets is the primary goal of the data mining techniques used hereafter; training is finished, and data-mining algorithms may have low computational complexity depending on the volume of the data, which helps try to identify FDIA in a smart grid. Fuzzy logic-based techniques for spotting network breaches have also been developed. For example, the developers of [25] constructed artificial immune systems that recognize dangers like network flooding using fuzzy logic. Fuzzy logic is used to discriminate between illegal and legal traffic. The authors present a fuzzy logic-based technique for pinpointing jammer attacks. This serves as yet another example of how fuzzy logic can be used to identify cyberattacks. This method uses the precise channel evaluation, the low packet ratio, and the received signal intensity to ascertain if the connection loss was due to jamming. They had some perfect ideas for intermittent and persistent jamming. Fuzzy logic was combined with other methods [26] to recognize different cyberattacks. Another crucial AI-based way is evolutionarily based algorithms. They are widely used for global advancement. Well-known evolutionary algorithms include genetic algorithms as examples. This kind of program can simulate how evolution and natural selection work. A genetic algorithm-based technique with two steps--training and detection--was proposed by the authors of [27]. They used a genetic algorithm in their research to remove all but the essential components of the detecting process. The authors conclude that this tactic works well for various network intrusions. The authors of [28] examined the potential effects of genetic algorithms on various machine-learning approaches. The simulation results show that genetic algorithms and the other three machine learning methods can identify FDIA. Figure 3 shows different components of the smart grid. Advanced metering infrastructure is essential to intelligent grid architecture. The primary purpose of AMI is to measure the energy consumption of integrated appliances and other devices, such as solar panels on roofs, gas meters, smart appliances, and water heaters.