PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer

• URL: http://arxiv.org/abs/2404.04886v2
• Date: Tue, 18 Jun 2024 03:05:01 GMT
• Title: PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer
• Authors: Xingyu Su, Xiaojie Zhu, Yang Li, Yong Li, Chi Chen, Paulo Esteves-Veríssimo,
• Abstract summary: 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. We also propose D&C-GEN to reduce the repeat rate of generated passwords, which adopts the concept of a divide-and-conquer approach.
• Score: 8.591143235694826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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