## 3 Password Generation Selection Model
### Password Generation Selection Model
Aiming at the research of password-generation technology, a password-generation model

Figure 11: The distribution of \(1\times 10^{8}\) passwords scores generated by the OMECDN model. based on an ordered Markov enumerator and critic discriminant network (OMECDN) was proposed in this paper. OMECDN optimizes the performance of password generation with a discriminative network based on the good statistical properties of OMEN. Moreover, the final password set was formed by the selected passwords with a higher score than the preset threshold, which guarantees the superiority of the hit rate of almost all ranges of combinations of passwords over the initial password set. When \(10^{9}\) passwords were generated, the hit rate reached 51.91%, which is 4.07% higher than the OMEN model and 84.71% higher than the PassGAN model. In the case of generating \(10^{7}\) passwords, the password hit entries generated by the OMECDN model were 25.18% higher than the password hit entries generated by the OMEN model and 243.58% higher than the password hit entries generated by the PassGAN model. The OMECDN model is better than the OMEN model and the PassGAN model and is a better password-generation model. Future work can focus on the distribution of the score output by the neural-network module. Studying the output of the neural-network module can discover the internal working mode of the neural network and determine what kind of structure of password score value can exceed the threshold. This will have a positive effect on improving the research of password-construction strategies. Conceptualization, L.Z. ; methodology, J.J.; software, J.J.; validation, J.J. and L.L. ; formal analysis, J.J.; investigation, L.L. ; resources, L.Z. ; data curation, L.L. ; writing--original draft preparation, J.J.; writing--review and editing, J.J.; visualization, L.L. ; supervision, A.Z. ; project administration, A.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Sichuan Science and Technology Program under Grant 2021 YFG0159 & 2022 YFG0171, in part by the Fundamental Research Funds for the Central Universities 2021 SCU12136. Not applicable. The authors declare no conflict of interest. ## References

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