PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer
<|reference_start|>PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer: Amidst the surge in deep learning-based password guessing models, challenges of generating high-quality passwords and reducing duplicate passwords persist. To address these challenges, we present PagPassGPT, a password guessing model constructed on Generative Pretrained Transformer (GPT). It can perform pattern guided guessing by incorporating pattern structure information as background knowledge, resulting in a significant increase in the hit rate. Furthermore, we propose D&C-GEN to reduce the repeat rate of generated passwords, which adopts the concept of a divide-and-conquer approach. The primary task of guessing passwords is recursively divided into non-overlapping subtasks. Each subtask inherits the knowledge from the parent task and predicts succeeding tokens. In comparison to the state-of-the-art model, our proposed scheme exhibits the capability to correctly guess 12% more passwords while producing 25% fewer duplicates.<|reference_end|>
@article{su2024pagpassgpt:,
    title={PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained
  Transformer},
    author={Xingyu Su, Xiaojie Zhu, Yang Li, Yong Li, Chi Chen, Paulo
  Esteves-Ver'issimo},
    journal={arXiv preprint arXiv:2404.04886},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04886},
    primaryClass={cs.CR cs.AI}
}