# A Deep Learning-Based Password Security Evaluation Model
## 4 Experiment and Evaluation
### Experiment Results and Performance Evaluation  
In order to verify the performance of the password security evaluation model that was selected earlier, the classification accuracy was compared with the existing password security evaluation indicators. The security evaluation score according to each security evaluation method and whether the passwords have actually been leaked were compared by using the test data of Table 3. They are as shown in Table 7.  
Figure 10: Performance graph of the evaluation model according to the depth of the hidden layer: (**a**) learning time according to the depth of the hidden layer; (**b**) loss according to the depth of the hidden layer; (**c**) accuracy according to the depth of the hidden layer. The test data in Table 7 used 4814 passwords that have not been leaked and 8530 passwords that have been leaked. When the security of a password is evaluated, if a password that has been leaked is evaluated as a password that has not been leaked it may cause the user to mistake a password that has been leaked to have high security. Therefore, the number of passwords that were leaked was given a bit more consideration when the test data was collected. In Table 7, the evaluation model proposed in this paper classified 8351 out of 8530 passwords that have been leaked as having weak security. It also classified 4425 out of 4814 passwords that have not been leaked as having strong security. This is classification accuracy of 95.74%. However, for Levenshtein distance in Table 7, only 1484 passwords that have been leaked were classified as weak security passwords, and 4768 of the passwords that have not been leaked were classified as ones with strong security. Since the classification accuracy of 95.74%, the accuracy of 95.74% is not as good as the security of the password is not as good as the security of the password is not as good as the security of the password is not as good as the security of the password. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Nodes** & **Time (ms)** & **Loss (MSE)** & **Accuracy (\%)** \\ \hline
1 & 29,442 & 0.25 & 50 \\
2 & 43,968 & 0.0423 & 94.934 \\
4 & 55,314 & 0.0426 & 94.926 \\
8 & 61,121 & 0.0422 & 94.926 \\
16 & 78,198 & 0.0422 & 94.937 \\
32 & 84,113 & 0.0422 & 94.937 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance table of the evaluation model according to the number of nodes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Leak or Not** & **Security Evaluation Score** & **Count** \\ \hline \multirow{3}{*}{Proposed evaluation model} & Leaked password & Weak & 8351 \\  & & Strong & 179 \\ \cline{2-4}  & \multirow{3}{*}{Not leaked password} & Weak & 389 \\  & & Strong & 4425 \\ \hline \multirow{6}{*}{Zxcvbn point} & \multirow{3}{*}{Leaded password} & Very weak & 488 \\  & & Weak & 7611 \\  & & Average & 428 \\  & & Strong & 3 \\  & & Very strong & 0 \\ \cline{2-4}  & \multirow{3}{*}{Not leaked password} & Very weak & 0 \\  & & Weak & 713 \\  & & Average & 657 \\  & & Strong & 784 \\  & & Very strong & 2660 \\ \hline \multirow{6}{*}{LUDS requirement} & \multirow{3}{*}{Not leaked password} & Very weak & 470 \\  & & Weak & 492 \\ \cline{1-1}  & & Average & 791 \\ \cline{1-1} \cline{2-4}  & \multirow{3}{*}{
\begin{tabular}{c} Strong \\ Very strong \\ \end{tabular} } & Strong & 617 \\ \cline{1-1}  & & Very strong & 2444 \\ \hline \multirow{6}{*}{Levenshtein distance} & Leaked password & Weak & 1484 \\ \cline{1-1}  & & Strong & 7046 \\ \cline{1-1} \cline{2-4}  & \multirow{3}{*}{Not leaked password} & Weak & 46 \\ \cline{1-1}  & & Strong & 4768 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison for password security evaluation performance. accuracy was 46.85%, it was confirmed that the Levenshtein distance had lower reliability compared to the proposed evaluation model.