PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer
arxiv(2024)
摘要
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
duplicates.
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