论文标题
关于密码猜测的深度学习，一项调查
On Deep Learning in Password Guessing, a Survey
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，以代表实际威胁。但是，这种方法需要难以复制的特定领域知识和专业知识。本文比较了不需要域知识或对用户密码结构和组合的假设的各种基于深度学习的密码猜测方法。所涉及的模型类别是复发性神经网络，生成对抗网络，自动编码器和注意机制。此外，我们提出了一种有前途的研究实验设计，以使用IWGAN的变体在非目标离线攻击下进行密码猜测。使用这些高级策略，我们可以增强密码安全性并创建更准确，更有效的密码强度计。
The security of passwords is dependent 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 be representative of the actual threat. This approach, however, needs domain-specific knowledge and expertise that are difficult to duplicate. This paper compares various deep learning-based password guessing approaches that do not require domain knowledge or assumptions about users' password structures and combinations. The involved model categories are Recurrent Neural Networks, Generative Adversarial Networks, Autoencoder, and Attention mechanisms. Additionally, we proposed a promising research experimental design on using variations of IWGAN on password guessing under non-targeted offline attacks. Using these advanced strategies, we can enhance password security and create more accurate and efficient Password Strength Meters.
