论文标题
GNPASSGAN：改进的生成对抗网络，用于拖网直线密码猜测
GNPassGAN: Improved Generative Adversarial Networks For Trawling Offline Password Guessing
论文作者
论文摘要
密码的安全取决于对攻击者使用的策略的透彻理解。不幸的是，现实世界中的对手使用务实的猜测策略，例如字典攻击，在密码安全研究中很难模拟。字典攻击必须仔细配置和修改以表示实际威胁。但是，这种方法需要难以复制的特定领域知识和专业知识。本文回顾了各种基于深度学习的密码猜测方法，这些方法不需要域知识或有关用户密码结构和组合的假设。它还引入了GNPASSGAN，这是一种基于生成对抗网络的密码猜测工具，用于拖动离线攻击。与最先进的盘子型号相比，Gnpassgan能够猜测88.03 \％的密码更多，并生成31.69 \％的重复。
The security of passwords depends on a thorough understanding of the strategies used by attackers. Unfortunately, real-world adversaries use pragmatic guessing tactics like dictionary attacks, which are difficult to simulate in password security research. Dictionary attacks must be carefully configured and modified to represent an actual threat. This approach, however, needs domain-specific knowledge and expertise that are difficult to duplicate. This paper reviews various deep learning-based password guessing approaches that do not require domain knowledge or assumptions about users' password structures and combinations. It also introduces GNPassGAN, a password guessing tool built on generative adversarial networks for trawling offline attacks. In comparison to the state-of-the-art PassGAN model, GNPassGAN is capable of guessing 88.03\% more passwords and generating 31.69\% fewer duplicates.
