# Location-Aware Measurement for Cyber Mimic Defense: You Cannot Improve What You Cannot Measure
## 5 Simulation
We also performed a comparative analysis of the transfer probabilities between nodes, part of which is visualized in Figure 4. The squares within the \(i\)-th row and \(j\)-th column of the figure represent the transfer probability from node \(i\) to node \(j\), with color intensity denoting the probability magnitude Comparing the calculated and experimental results, we observe a close alignment. All of these indicate that our proposed metrics have excellent practical application value. Figure 3: The relationship between failure risk and number of attacks. In addition, considering the scale of the network and the estimated deviation of \(p_{1}\) and \(p_{2}\), we calculated the coefficients of correlation between the theoretical vulnerability and the actual vulnerability and summarized them in Tables 1-3. As shown in Table 1, we tested the effectiveness of evaluation metrics for nodes and communities under different network scales, and we can see that when the network scale gradually expands, our indicators fit well with the actual situation, especially for our community-based research ideas. In Tables 2 and 3, we consider the effect of the metrics when the estimated values \(p_{1}\) and \(p_{2}\) in the transition probability mentioned above exhibit some deviation. The majority of data in these tables exceed 0.7, signifying a robust correlation coefficient. This implies that despite potential estimation deviation, our metrics retain substantial error tolerance. Finally, two comparative experiments are given, and the experimental data are shown in Table 4. We compared with two related models [13; 20] to further substantiate our model's performance. The outcomes demonstrate that our method yields favorable results for calculating the correlation coefficients of nodes and communities within the intricate network environment. Specifically, our approach outperforms other methods in terms of nodes, and as the number of nodes progressively increases, the superiority of our model becomes particularly pronounced within the community context. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Number of** & **Number of** & **Average Coefficient of** & **Average Coefficient of** \\
**Trials** & **Nodes** & **Simulated Attacks** & **Correlation for Nodes** & **Correlation for Communities** \\ \hline
100 & 100 & 10,000 & 0.73 & 0.91 \\ \hline
100 & 200 & 20,000 & 0.71 & 0.94 \\ \hline
50 & 500 & 50,000 & 0.71 & 0.97 \\ \hline
20 & 1000 & 100,000 & 0.70 & 0.97 \\ \hline
20 & 2000 & 200,000 & 0.69 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average coefficient of correlation for nodes and communities under different variables. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Cefficient** & **Estimated** & **Cof \(p_{1}\)** & **Estimated** & **Cof \(p_{2}\)** & **Estimated** & **Cof \(p_{1}\)** & **Estimated** & **Cof \(p_{2}\)** & **Estimated** & **Cof \(p_{1}\)** & **Estimated** & **Estimated** & **Estimated** \\
**Deviation of \(p_{1}\)** & \(-5\%\) & \(-4\%\) & \(-3\%\) & \(-2\%\) & \(-1\%\) & \(0\%\) & \(1\%\) & \(2\%\) & \(3\%\) & \(4\%\) & \(5\%\) \\ \hline \(-5\%\) & 0.92 & 0.98 & 0.56 & 0.99 & 0.95 & 0.99 & 0.83 & 0.94 & 0.99 & 0.91 & 0.86 \\ \hline \(-4\%\) & 0.95 & 0.96 & 0.97 & 0.96 & 0.96 & 0.97 & 0.98 & 0.96 & 0.67 & 0.96 & 0.99 \\ \hline \(-3\%\) & 0.92 & 0.95 & 0.95 & 0.97 & 0.74 & 0.99 & 0.96 & 0.98 & 0.97 & 0.74 & 0.96 \\ \hline \(-2\%\) & 0.98 & 0.89 & 0.99 & 0.83 & 0.92 & 0.96 & 0.93 & 0.85 & 0.92 & 0.96 & 0.94 \\ \hline \(-1\%\) & 0.78 & 0.95 & 0.88 & 0.95 & 0.71 & 0.99 & 0.94 & 0.92 & 0.98 & 0.93 & 0.78 \\ \hline \(0\%\) & 0.96 & 0.95 & 0.87 & 0.98 & 0.88 & 0.99 & 0.84 & 0.90 & 0.97 & 0.91 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coefficient of correlation for nodes considering the estimated deviation of \(p_{1}\) and \(p_{2}\). Figure 4: Transfer probability between nodes.