OMECDN: A Password-Generation Model Based on an Ordered Markov Enumerator and Critic Discriminant Network

At present, static text passwords are still the most widely-used identity authentication method. Password-generation technology can generate large-scale password sets and then detect defects in password-protection mechanisms, which is of great significance for evaluating password-guessing algorithms. However, existing password-generation cannot ignore low-quality generated set, will lead to low-efficiency guessing. In this paper, a model based on an ordered Markov enumerator critic discriminant network (OMECDN) proposed, where via (OMEN) according probability combination passwords. OMECDN optimizes performance generation with discriminative good statistical properties OMEN. Moreover, final set formed by selected higher score than preset threshold, guarantees superiority hit rate almost all ranges combinations over initial set. Finally, experiments show that achieves qualitative improvement metrics. particular, regarding 107 RockYou dataset, matching entries 25.18% 243.58% those OMEN PassGAN model, respectively.