# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 7 Conclusions  
In view of increasing security issues, it is necessary to apply situational awareness methods to provide comprehensive security protection for smart grids. However, there are still many inadequacies in the current situational awareness applications. The heterogeneous connections of massive power terminals with multiple communication protocols and multiple terminal interactions are added to the burden of the power network. In addition, the smart grids based on the power cloud master station pattern may not be timely for threat response. Therefore, the effective management of massive heterogeneous terminals, high bandwidth, and low latency situational awareness methods need to be updated urgently. Based on this, this paper has proposed an edge computing paradigm applied in smart grids' security situational awareness. By deploying intelligent edge agents with specific computing resources close to the power terminals, the problem of multiple power terminals and immediate threat response has been effectively solved. On the other hand, in the pursuit of efficient situational awareness methods, traditional security situational awareness methods cannot effectively solve the problems such as dynamic changes in environmental and continuous situational elements' state space. Together with the high-security requirements of smart grids, we have proposed a multi-agent deep deterministic policy gradient algorithm based on deep reinforcement learning to solve the above problems. The situational elements of wide-area situational awareness are considered the state space of the algorithm, the awareness behavior as the action space, the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Deviation** \\ **Amplitude (\%)** \\ \end{tabular} & **Label** & \begin{tabular}{c} **Number of** \\ **Testing Data** \\ \end{tabular} & \begin{tabular}{c} **Identified to** \\ **Be** **Normal** \\ \end{tabular} & \begin{tabular}{c} **Identified to** \\ **Be** **Normal** \\ \end{tabular} & \begin{tabular}{c} **False** \\ **Rejection Rate** \\ \end{tabular} & \
\begin{tabular}{c} **False** \\ **Acceptance** \\ \end{tabular} &
\begin{tabular}{c} **Detection** \\ **Accuracy** \\ \end{tabular} \\ \hline \multirow{2}{*}{0.05} & Normal & 500 & 437 & 63 & \multirow{2}{*}{6.3} & \multirow{2}{*}{21.7} & \multirow{2}{*}{72.0} \\ \cline{2-2} \cline{6-6}  & Compromised & 500 & 217 & 283 & & & \\ \hline \multirow{2}{*}{0.1} & Normal & 500 & 500 & 0 & 7.7 & 92.3 \\ \cline{2-2} \cline{6-6}  & Compromised & 500 & 77 & 423 & & & \\ \hline \multirow{2}{*}{0.3} & Normal & 500 & 500 & 0 & 4.3 & 95.7 \\ \cline{2-2} \cline{6-6}  & Compromised & 500 & 43 & 457 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the proposed model for FDI attack detection in the IEEE bus-118 system. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Deviation** \\ **Amplitude (\%)** \\ \end{tabular} & **Label** & \begin{tabular}{c} **Number of** \\ **Testing Data** \\ \end{tabular} & \begin{tabular}{c} **Identified to** \\ **Be** **Normal** \\ \end{tabular} & \begin{tabular}{c} **Identified to** \\ **Be** **Normal** \\ \end{tabular} & \
\begin{tabular}{c} **False** \\ **Rejection Rate** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **False** \\ **Acceptance** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{2}{*}{0.05} & Normal & 500 & 436 & 64 & \multirow{2}{*}{6.4} & \multirow{2}{*}{7.8} & \multirow{2}{*}{85.7} \\ \cline{2-2} \cline{6-6}  & Compromised & 500 & 78 & 421 & & & \\ \hline \multirow{2}{*}{0.1} & Normal & 500 & 432 & 68 & \multirow{2}{*}{6.8} & \multirow{2}{*}{1.1} & \multirow{2}{*}{92.1} \\ \cline{2-2} \cline{6-6}  & Compromised & 500 & 11 & 489 & & & \\ \hline \multirow{2}{*}{0.3} & Normal & 500 & 498 & 2 & \multirow{2}{*}{0.2} & \multirow{2}{*}{0} & \multirow{2}{*}{99.8} \\ \cline{2-2} \cline{6-6}  & Compromised & 500 & 0 & 500 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the proposed model for FDI attack detection in the IEEE bus-14 system.