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.

PagPassGPT 是一种基于 Generative Pretrained Transformer (GPT) 的密码猜测模型，利用模式结构信息进行策略引导猜测，从而显著提高猜中率；此外，我们提出了 D&C-GEN 来减少生成密码的重复率，采用分治法思想将猜测密码的主任务递归地划分为非重叠子任务，并从父任务中继承知识并预测后续标记；与现有模型相比，我们的方案在正确猜测密码的同时减少了 25% 的重复率。

PagPassGPT: 基于模式引导的密码猜测与生成预训练变压器

PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained  Transformer

Password guessing approaches via deep learning have recently been
investigated with significant breakthroughs in their ability to generate novel,
realistic password candidates. In the present work we study a broad collection
of deep learning and probabilistic based models in the light of password
guessing: attention-based deep neural networks, autoencoding mechanisms and
generative adversarial networks. We provide novel generative deep-learning
models in terms of variational autoencoders exhibiting state-of-art sampling
performance, yielding additional latent-space features such as interpolations
and targeted sampling. Lastly, we perform a thorough empirical analysis in a
unified controlled framework over well-known datasets (RockYou, LinkedIn,
Youku, Zomato, Pwnd). Our results not only identify the most promising schemes
driven by deep neural networks, but also illustrate the strengths of each
approach in terms of generation variability and sample uniqueness.

本文研究了基于深度学习和概率的密码猜测模型，提出了基于 VAE 的最新深度学习模型，在经典数据集上进行了实证分析并比较了各种方法的生成性能和样本特征。

密码生成的生成式深度学习技术

Generative Deep Learning Techniques for Password Generation

Learning useful representations from unstructured data is one of the core
challenges, as well as a driving force, of modern data-driven approaches. Deep
learning has demonstrated the broad advantages of learning and harnessing such
representations. In this paper, we introduce a deep generative model
representation learning approach for password guessing. We show that an
abstract password representation naturally offers compelling and versatile
properties that can be used to open new directions in the extensively studied,
and yet presently active, password guessing field. These properties can
establish novel password generation techniques that are neither feasible nor
practical with the existing probabilistic and non-probabilistic approaches.
Based on these properties, we introduce:(1) A general framework for conditional
password guessing that can generate passwords with arbitrary biases; and (2) an
Expectation Maximization-inspired framework that can dynamically adapt the
estimated password distribution to match the distribution of the attacked
password set.

本文介绍了一种基于深度生成模型表示学习的密码猜测方法，提出了一般的有条件密码猜测框架和动态自适应估计密码分布的 EM 框架。