## 6 Conclusions  
In this paper, we proposed a deep learning-based method to build an effective password cracking dictionary from multiple models' password candidates. Our model is particular in that it is the first approach to apply a GAN discriminator to the password cracking domain. Generally, because GANs are a generative deep learning model, the generator of the GAN is used. However, in our model (REDPACK), the discriminator of the GAN is a core component. For accomplishing the goal of making the discriminator of the GAN choose more realistic password candidates, we adopt the RaSGAN-GP cost function, which was proposed by [PERSON] [13]. Through several password-cracking experiments, we showed that the deep learning-based password candidate selector, REDPACK, outperformed every other individual password candidate-guessing model (Hashcat, PCFG, OMEN, and rPassGAN) in password cracking performance experiments. These experiments also showed the effectiveness of the GAN's discriminator. Finally, we demonstrated that our custom set of rules for REDPACK improved its cracking performance, compared to ready-made rules such as the best64. Significantly, this set of rules were successfully applied to REDPACK and rPassGAN. However, in the case of PCFG and OMEN, this set of rules worsened the cracking performance compared to the best64. Although there are some defensive studies [34; 35; 36; 37] that counter offline dictionary attacks, REDPACK, by facilitating the creation of a small and effective password cracking dictionary, enabled us to execute a fast attack. From a security perspective, the outperformed cracking model's candidates can enhance password strength estimators, such as Zxcvbn [7], by filtering the guessable password candidates from our previous study [12]. Therefore, it is clear that the candidates produced by REDPACK can fortify the password strength estimator. REDPACK can be used for both attacking and defending the security of passwords. Through the rPassGAN [8; 12] and REDPACK studies, we achieved a new way to improve password cracking performance by incorporating both the generator and discriminator of a GAN. In the future, we will continue to conduct research into improving the performance of REDPACK. Conceptualization, S.N. ; methodology, S.N. ; software, S.N., S.J. ; validation, S.N, S.J. ; data curation, S.N. ; writing--original draft preparation, S.N. ; writing--review and editing, J.M. ; supervision, J.M. ; All authors have read and agreed to the submitted version of the manuscript. This research received no external funding. This research was supported by a Korea University Grant. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset ID** & **Rule** & **REDPACK\({}_{\text{U3}}\)** & **PCFG** & **OEMN** & **rPassGAN** \\ \hline \multirow{3}{*}{1-1} & best64 & 69,473 & 69,133 & 53,224 & 33,278 \\  & custom & 72,387 (4\%) & 66,793 (\(-\)3.8\%) & 55,391 (4\%) & 35,323 (6\%) \\ \multirow{3}{*}{2} & best64 & 35,699 & 14,796 & 8184 & 5858 \\  & custom & 43,442 (22\%) & 16,014 (8\%) & 11,106 (36\%) & 8568 (46\%) \\ \multirow{3}{*}{3} & best64 & 17,475 & 14,796 & 8184 & 3871 \\  & custom & 22,042 (26\%) & 16,013 (8.2\%) & 11,106 (35\%) & 6427 (66\%) \\ \multirow{3}{*}{4} & best64 & 10,819 & 7830 & 4185 & 2588 \\  & custom & 13,309 (23\%) & 8482 (8\%) & 5179 (23\%) & 3077 (19\%) \\ \multirow{3}{*}{5} & best64 & 5984 & 3253 & 2042 & 1819 \\  & custom & 7058 (17\%) & 3558 (9\%) & 2369 (16\%) & 2071 (14\%) \\ \multirow{3}{*}{6} & best64 & 3252 & 1376 & 1070 & 1500 \\  & custom & 3728 (14\%) & 1491 (8\%) & 1145 (7\%) & 1555 (4\%) \\ \multirow{3}{*}{7} & best64 & 1803 & 523 & 661 & 1210 \\  & custom & 2050 (13\%) & 565 (8\%) & 671 (1.5\%) & 1233 (2\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cracking performance of custom rule set for REDPACK; this custom set of rules was effective when applied to REDPACK.