A GAN-based approach for password guessing

Password is the most widely used authenticate method. Individuals ordinarily have numerous passwords for their documents or devices, and, in some cases, they need to recover them with password guessing tools. Most popular guessing tools require a dictionary of common passwords to check with password hashes. Thus, generative adversarial networks (GANs) are suitable choices to automatically create a high-quality dictionary without any additional information from experts or password structures. One of the successful GAN-based models is the PassGAN. However, existing GAN-based models suffer from the discrete nature of passwords. Therefore, we proposed and evaluated two improvement of the PassGAN model to tackle this problem: the GS-PassGAN model using Gumbel-Softmax relaxation and the S-PassGAN using a smooth representation of a real password obtained by an additional Auto-Encoder. Experiment results on three different popular datasets show that the proposed method is better than the PassGAN both in the standalone and combining cases. Moreover, the matching rate of the proposed method can be increased by more than 5%.