## 3 Password Generation Selection Model
### Password Generation Selection Model
### OMECDN Model Evaluation

We used the OMEN-5 gram model trained in Section 4 as the password generation module implementation algorithm in the password generation selection model and the PassGAN neural-network model discriminator trained in Section 4 as the password selec

Figure 5: Effects of training rounds on the repetition rate of PassGAN. tion module implementation algorithm in the password generation selection model to form the OMECDN model. The score thresholds \(\gamma\) were set to \(-1.2\), \(-1.3\), and \(-1.4\), respectively. According to Algorithm 1, the password set was generated, and the password set generated by the OMECDN model was compared with the Rockyou test set according to Equation (7). The results are shown in Figure 6.

In the legend in Figure 6, OMECDNn5i200000s13 represents the implementation of the password generation module of the OMECDN model. In the N-gram of OMEN, N is 5, the number of training rounds is 200,000, and the score threshold constant \(\gamma\) is \(-1.3\). The other legends are also the same. The results show that, when the number of training rounds was 400,000 and the score threshold constant \(\gamma\) was set to \(-1.3\), the OMECDN model had the highest hit rate when the same number of password sets were generated. The specific numbers of hits of the three models are shown in Table 3.

Using the same experimental method, the password generation fitting experiment was performed on the Yahoo, 12306, and CSDN datasets. In the case of generating \(10^{5}\) passwords, the password results are shown in Figure 7.

The experimental data of password generation and fitting of different datasets show that, in the Rockyou, 12306, and CSDN datasets, OMECDNn5i200000s13 had the highest hit rate, and, in the Yahoo dataset, OMECDNn5i200000s12 had the highest hit rate. In summary, the OMECDN model performed better than the OMEN model and the PassGAN model. The network model is a better password-generation model. Figure 3.0 The hit rate amounts for the three models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Guesses**} & \multicolumn{3}{c}{**Models**} & \multicolumn{3}{c}{**Improvement**} \\ \cline{2-5}  & \begin{tabular}{c} **OMEN** \\ **(\(Hit\_Rate\))** \\ \end{tabular} & \begin{tabular}{c} **PassGAN** \\ **(\(Hit\_Rate\))** \\ \end{tabular} & \begin{tabular}{c} **OMECDN** \\ **(\(Hit\_Rate\))** \\ \end{tabular} & \
\begin{tabular}{c} **(OMECDN-OMEN)/OMEN** \\ **(\(Hit\_Rate\))** \\ \end{tabular} &
\begin{tabular}{c} **(OMECDN-PassGAN)/** \\ **\(\ast\) 100\%** \\ \end{tabular} \\ \hline \(10^{5}\) & 10,045 (0.51\%) & 1146 (0.06\%) & 11,272 (0.57\%) & 12.22\% & 883.60\% \\ \(10^{6}\) & 53,257 (2.69\%) & 10,539 (0.53\%) & 60,123 (3.04\%) & 12.89\% & 470.48\% \\ \(10^{7}\) & 199,507 (10.09\%) & 72,691 (3.68\%) & 249,750 (12.63\%) & 25.18\% & 243.58\% \\ \(10^{8}\) & 526,296 (26.62\%) & 264,978 (13.40\%) & 559,439 (28.29\%) & 6.30\% & 111.13\% \\ \(10^{9}\) & 986,322 (49.88\%) & 555,724 (28.10\%) & 1,026,474 (51.91\%) & 4.07\% & 84.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hits of the three models. ### OMECDN Model Score Distribution

In Section 3.2, we introduced that OMEN realizes the generation of combined passwords based on the probability of multiple N-gram character sequences (the method is to convert all N-gram sequence probabilities \(p\) into discrete fixed intervals). Understanding this section of the interval helps to read the subsequent figures. In this section, we analyze the distribution of teh password scores in the password set generated by the password generation module algorithm in the OMECDN model. The password scores are divided into 41 intervals, which are:

\[(-\infty,-1.9],(-1.9,-1.8],(-1.8,-1.7],\ldots,(1.8,1.9],(1.9,2.0],(2.0,+\infty)\]

The first 40 intervals are half-open intervals. Except for the first interval and the last interval, the length of each interval is 0.1.