A New Password Cracking Model with Generative Adversarial Networks

Abstract

Owing to the generality and importance of the password as a means of authentication, many studies have addressed password-strength evaluation methods and password cracking methods. Recently, the generative adversarial networks approach to enhance password guessing (PassGAN) has been proposed as a password cracking method in research that is based on generative adversarial networks (GAN). The results of this study have received substantial attention. In this paper, we propose the use of a recurrent neural networks-based (RNN) GAN, which comprises the use of the improved Wasserstein GAN (IWGAN) cost function. These models that combine the RNN with IWGAN perform better than PassGAN. We have conducted experiments to compare the performance of our proposed model with that of PassGAN and analyzed the results. Using these analyses, we confirmed that our proposed models exhibited a password cracking performance improvement of 5–10% more than that of PassGAN.