论文标题
通过深度学习来解释的概率密码强度计
Interpretable Probabilistic Password Strength Meters via Deep Learning
论文作者
论文摘要
概率密码强度计已被证明是测量密码强度的最准确的工具。不幸的是，通过构造，它们仅限于制作不透明的安全性估计，该估计在密码组合过程中未能完全支持用户。在目前的工作中，我们将第一步迈向破解这一引人入胜的仪表的清晰度障碍。我们表明，概率密码计本来就拥有描述密码强度和密码结构之间发生的潜在关系的能力。在我们的方法中，编写密码的每个字符的安全贡献都被删除，并用于为用户提供明确的细粒反馈。此外，与现有的启发式结构不同，我们的方法没有任何人类偏见，更重要的是，其反馈具有概率的解释。在我们的贡献中：（1）我们制定了可解释的概率密码强度计； （2）我们描述了如何通过适合客户端可操作性的高效且轻巧的深度学习框架来实现它们。
Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user during the password composition. In the present work, we move the first steps towards cracking the intelligibility barrier of this compelling class of meters. We show that probabilistic password meters inherently own the capability of describing the latent relation occurring between password strength and password structure. In our approach, the security contribution of each character composing a password is disentangled and used to provide explicit fine-grained feedback for the user. Furthermore, unlike existing heuristic constructions, our method is free from any human bias, and, more importantly, its feedback has a probabilistic interpretation. In our contribution: (1) we formulate interpretable probabilistic password strength meters; (2) we describe how they can be implemented via an efficient and lightweight deep learning framework suitable for client-side operability.
