## 2 Related Work  
[PERSON] et al. [4], in their study, provided a machine learning-based approach for identifying DoS attacks on smart grids to address the intrusion problem. This approach collects real-time data from a smart meter and a data server. DoS attacks are identified and detected using SVM classifier-trained models, which use feature selection and PCA dimension reduction to select more characteristics. Experiments on the public domain dataset show that the SVM classification model outperforms the Naive Bayesian Network and the decision tree  
Figure 1: Cyberattack scenario in smart grid. classification methods. The model presented in this paper primarily employs the machine learning algorithm (ML) to detect DoS attack behavior or expected behavior, thereby enhancing the smart grid's security. In their work, [PERSON] et al. [5] showed how they first collected the state estimator's normal and stealthy attacked operational points. Learning (historical) data come from network active power flow monitoring. Historical data are well separated from those under attack when projected into a low-dimensional space. Machine learning methods can detect stealthy erroneous data injection in the state estimator. The authors identify attacked and safe operation modes using supervised and unsupervised learning. The suggested algorithms identify stealthy fraudulent data injection in numerical results. Transmission line and generator outages are examples of non-cyberattack failures. This article could leverage additional advanced machine learning and data mining methods to discover power network problems. [PERSON] et al. [6] demonstrated a man-in-the-middle-based attack scenario in which an attacker monitors process communication between control systems and field devices, injects spoofed data, and corrupts the data, for example, by sending bogus commands to the field devices. The authors analyze the data collected under both standard and attack settings to extract domain-specific information for detection techniques, and they show that the given attack scenario is applicable in a physical smart grid laboratory context. [PERSON] et al. [7] created a technique to detect cyber intrusions in smart grids based on semi-supervised anomaly detection and deep representation learning using PMU measurements that span physical and cyber realms. Since supervised anomaly detection methods rely solely on examples of regular occurrences to train detection models, semi-supervised anomaly detection methods are ideal for detecting events of unknown attack types. They used publicly available datasets of attacks on the power grid to evaluate various techniques. Semi-supervised algorithms are superior to standard supervised algorithms in detecting attack events. Furthermore, their findings demonstrate that deep autoencoder-based representation learning can improve the detection performance of semi-supervised algorithms. In their work, [PERSON] et al. [8] comprehensively analyzed the effects of forged data attacks on a smart grid in their survey. They summed up the state-of-the-art techniques for detection based on machine learning by categorizing them into three different types of attacks. They established the critical need for cutting-edge machine learning methods combining high resilience and efficiency. The above-mentioned work emphasizes the need to use machine learning techniques to improve the security of smart grids against cyber threats. Many technologies, such as SVM classification, deep representation learning, and semi-supervised anomaly detection, are used to detect and mitigate many cyberattacks, including DoS and data injection. These technologies use real-time data from smart meters, data servers, and power flow monitoring to detect abnormal behavior and assure the integrity and stability of smart grid operations. Furthermore, powerful machine learning approaches are widely acknowledged as necessary for efficiently addressing growing cyberattacks.