## 2 Literature Review
A man-in-the-middle attack happens in a smart grid when an attacker physically placed between two authorized devices connects to and sends communication between them. While the devices seem to speak, the attacker includes a third device in the conversation. These attacks' main goals are to interrupt network activity, change data while it is being transmitted, or obtain unauthorized access to sensitive data [53]. The security and privacy of a network may be risked if MITM is used. Social engineering is another cyberattack that could be used against smart grid technology. These attacks aim at the application layer and potentially risk the system's privacy [54]. According to the authors, social engineering is the greatest threat to information security. They explored social engineering techniques such as rob calls, phone/windows fraud, and reverse social engineering. Each of these attacks aims to trick victims into disclosing private information. These risks put users at \begin{table}
\begin{tabular}{c c c} \hline \hline
**AI Technique** & **Advantages** & **Disadvantages** \\ \hline \multirow{7}{*}{ANN} & AI methods are more complex to understand than \\  & artificial neural networks. A multi-step process known & It has a higher computational cost and tends \\  & as information technology is used to analyze data and & to overload. The model creation process is \\  & look for a potentially unexpected pattern. It works with & based on empirical research [45]. \\  & a range of teaching techniques [45]. & Large data sets are too complicated for this \\  & Control parameters in ANN keep the model without & Large data sets are too complicated for this \\  & being too accurate. This works best when there are & method. Using this method when there are \\  & apparent differences between the groups in the data set. & overlapping categories is not practical. \\  & The kernel technique makes it quick and easy to become & Testing is a slow process [46]. \\  & By combining the learning capabilities of an ANN with & \\  & fuzzy systems, a neuro-fuzzy system may automatically & Depending on the number of fuzzy rules that \\  & create fuzzy if-then rules and optimize their parameters. & were initially used. More calculations must \\  & This fixes the fundamental problems that have & be done as unclear regulations are added. \\  & prevented designing fuzzy systems up to now [47]. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of AI Methods on smart grid. risk of having their personal information stolen for impersonation purposes, which can reduce their sense of security. A well-known passive attack on communication routes for smart grids is listening [54]. It goes after the network layer and affects the smart grid's specific privacy requirements. According to the attacks occur when a malicious user listens in on a conversation between two nodes on a LAN network to gather information. A user could use this sensitive data maliciously to interfere with the network. These assaults compromise the network's security. A smart grid's physical and data link layers are known targets for timing-sensitive attacks [55]. The TSA is capable of managing, monitoring, and protecting large regions and 3-phase measuring devices. Synchronized measurements are required for numerous smart grid applications, and the vast majority of measuring instruments now come standard with GPS to provide accurate time information. These are vulnerable to spoofing attempts, just like other GPS-enabled devices. Smart grids require quick communication and control signals, making them more susceptible to cyberattacks such GPS spoofing and time-sensitive access [56]. By using hybrid brute force, reverse brute force, and credential stuffing, the presentation layer, session layer, or network layer can be compromised. Figure 6 shows the Cyber-Attack Classification. A \"password guessing attack\" is when an attacker attempts to guess or decode a user's username or passphrase to access the user's account or system.