Open AccessPGGAN: Improve Password Cover Rate Using the Controller
Author(s) -
Xiaozhou Guo,
Yi Liu,
Kaijun Tan,
Min Jin,
Huaxiang Lu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1856/1/012012
Subject(s) - discriminator , cover (algebra) , measure (data warehouse) , password , generator (circuit theory) , controller (irrigation) , computer science , data mining , computer security , telecommunications , engineering , power (physics) , physics , agronomy , quantum mechanics , detector , biology , mechanical engineering
Password generation model based on generative adversarial network usually has the problem of high duplicate rate, which further leads to low cover rate. In this regard, we propose PGGAN model. It sets up an additional controller network which is similar to the discriminator in the aspect of structure and function. The discriminator and the controller respectively learn the measure between the distribution of generated password with the real password distribution and the uniform distribution, and then use two measures to teach generator meanwhile. By changing the activation function and loss function of the controller, different measure functions can be selected. The experimental results show that compared with GAN, our PGGAN performs better both in cover rate and duplicate rate. Moreover, Wasserstein distance usually has a better effect to the other measure in model. Specifically, PGGAN with Wasserstein distance can increase the cover rate by 3.57% and reduce the duplicate rate by 30.85% on rockyou dataset.