PGTCN: A novel password-guessing model based on temporal convolution network

Abstract

Text passwords are the primary method for identity authentication. However, easy-to-remember passwords are vulnerable to password-guessing attacks. The study of password-guessing not only enhances the understanding of password security, but also promotes the improvement of password library security. Nowadays, deep learning based models have demonstrated their promising ability for password-guessing, e.g., Recurrent Neural Network (RNN) and its variants. However, RNNs are failed to parallelize and lack long memory, resulting in unsatisfactory guessing efficiency and effectiveness. Aiming to generate a high-quality password dictionary for password checking, we propose a temporal convolutional network-based password-guessing model named PGTCN. Specifically, in PGTCN we improve the password-guessing efficiency by introducing the Improved Temporal Convolutional Network (ITCN) which adopts residual learning and feature fusion techniques. PGTCN can automatically study the structure and characteristics of passwords and generate new passwords based on the learned knowledge. To verify the performance of PGTCN, we compare it with state-of-the-art models on six public password datasets. Evaluation results show that the password dictionary generated by the proposed PGTCN achieves the structure coverage rate up to 84%, and boosts the matching rate up to 22%. We conclude that the PGTCN could be regarded as a robust and efficient model for password guessing.