Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries

Abstract

Password security hinges on an accurate understanding of the techniques adopted by attackers. However, current studies mostly rely on probabilistic password models that are imperfect proxies of real-world guessing strategies. The main reason is that attackers rely on very pragmatic approaches such as dictionary attacks. Unfortunately, it is inherently difficult to correctly model those methods. To be representative, dictionary attacks must be thoughtfully configured according to a process that requires an expertise that cannot be easily replicated in password studies. The consequence of inaccurately calibrating those attacks is the unreliability of password security estimates, impaired by measurement bias. In the present work, we introduce new guessing techniques that make dictionary attacks consistently more resilient to inadequate configurations. Our framework allows dictionary attacks to self-heal and converge towards optimal attacks' performance, requiring no supervision or domain-knowledge. To achieve this: (1) We use a deep neural network to model and then simulate the proficiency of expert adversaries. (2) Then, we introduce automatic dynamic strategies within dictionary attacks to mimic experts' ability to adapt their guessing strategies on the fly by incorporating knowledge on their targets. Our techniques enable robust and sound password strength estimates, eventually reducing bias in modeling real-world threats in password security.