PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911921452613632 |
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| author | Su, Xingyu Zhu, Xiaojie Li, Yang Li, Yong Chen, Chi Esteves-Veríssimo, Paulo |
| author_facet | Su, Xingyu Zhu, Xiaojie Li, Yang Li, Yong Chen, Chi Esteves-Veríssimo, Paulo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04886 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer Su, Xingyu Zhu, Xiaojie Li, Yang Li, Yong Chen, Chi Esteves-Veríssimo, Paulo Cryptography and Security Artificial Intelligence 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. |
| title | PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2404.04886 |