## 1 Introduction
#### Relevant Literature
In recent years, AI-aided intelligent cyber-physical systems have been extensively adopted. To alleviate or degrade the adverse effects of cyber-attacks, many studies adopted in the literature are evaluated with the help of utilizing supervised machine learning (SML) techniques [28] for FDI attacks in the scope of the smart grid. Due to the vulnerability of the communication protocols of cyber-physical systems, cyber-attacks are generally executed on IoT-enabled parts [29; 30], and a combination of cutting-edge technology, such as AI and blockchain [31], has also been proposed. As summarized in Table 1, a summary of a detailed comparison of the current achieved studies is provided to make the paper more explicit. As of today, this proposed defense mechanism for EIs can be one of the few essential solutions in this developing area. This scheme is introduced to differentiate between voltage collapse or contingency, whether they are cyber-attacks against the system. The utilization of detection systems enables the proposed method to reveal broader applicability across topologies. Detecting cyber-attacks without deteriorating stability criteria can enhance the system's reliability. Results tests and long-term reliability assessments show that the proposed solutions effectively address the problems. This study presents an effective defense mechanism to protect power converters in PV-supported EIs from cyber-attacks. The main achievements and contributions of the study can be summarized as the proposal of the following points:  
1. An early warning detection system has been established using the ANN-based SML algorithm, which enhances system reliability and enables rapid intervention against cyber threats. 2. The proposed ANN structure has attained satisfactory accuracies with convenient training datasets. While ANN-I is tasked with detecting cyber threats, the other ANN-II supports the subsequent demand for delivered power, leading to a complementary scheme. 3. Within the scope of this study, it is demonstrated that ANN-based SML algorithms provide faster and more effective analysis mechanisms than traditional methods, \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author [Ref]-Year** & **System Description** & **Performance Metrics** \\ \hline [PERSON] et al. [32]-2025 & A deep learning model for detecting data integrity attacks in PV farms. & Highest accuracy of 99.8\% in detecting cyber-attacks \\ \hline [PERSON] et al. [33]-2022 & A deep learning method for detecting malicious attacks in SCADA systems. & Accuracy of 98.7\% in detecting cyber-attacks \\ \hline [PERSON] et al. [34]-2024 & Detection of cyber-attacks (data falsification attacks) in energy systems using the XGBoost algorithm. & Accuracy of 95.5\% algorithm \\ \hline [PERSON] et al. [35]-2022 & Detecting hidden attackers in PV systems with ML algorithm. & Accuracy of up to 95\% \\ \hline [PERSON] et al. [36]-2020 & Cyber-attack detection for PV systems. & Accuracy of 99.23\% and 0.9963 F1 score \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of detailed comparison table of related studies in cyber-attack detection. asserting their strong resistance to emerging threats through continuous learning and adaptation. 4. Future studies should focus on strengthening the resilience of critical infrastructures, such as energy systems, by emphasizing early cyber-attack detection. 5. Consequently, the study assesses measures to counter cyber-attacks in PV-supported microgrids, revealing that integrating ANNs and anomaly detection algorithms can significantly aid in early detection and prevention processes. 6. It is concluded that ANN-based solutions should be integrated with additional security layers and that human oversight is crucial for improving the accuracy of these systems. The rest of the paper is organized as follows: the description of the PV-powered EI structure is introduced in Section 2. Section 3 shows the scheme for the detection of cyber-attacks with an SML model.