Towards a Rigorous Statistical Analysis of Empirical Password Datasets
Towards a Rigorous Statistical Analysis of Empirical Password Datasets
复制标题
DOI：
10.1109/sp46215.2023.10179431
复制
发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Jeremiah Blocki;Peiyuan Liu
中科院分区：
文献类型：
--
作者：
Jeremiah Blocki;Peiyuan Liu
密码安全性的核心挑战是表征攻击者的猜测曲线，即攻击者在第一个G猜测中破解随机用户的密码的可能性是什么。在这项工作中，我们未知的用户密码的分配是遵循Kerkhoffs的主体并分析最佳攻击者的性能知道密码分发。 p}} $。我们的经验分析表明，即使是最先进的密码破解模型通常比攻击者可以根据其（部分）对密码分布的知识来优化其攻击的效率。 - 密码分发的不同模型，即经验密码分布和ZIPF定律。当猜测数g不大时，即g <n。但是，对于G的较大值，我们的经验分析的较大值表明经验分布（ZIPF定律）高估了攻击者的成功率我们的统计技术在上/下限制了密码节流机制的有效性（密钥拉伸），用于减少攻击者猜测的数量。最后，如果我们愿意关于用户响应密码限制的方式，我们可以使用统计技术来评估各种密码组成策略的有效性，从而限制用户可能选择的密码。
A central challenge in password security is to characterize the attacker's guessing curve i.e., what is the probability that the attacker will crack a random user's password within the first G guesses. A key challenge is that the guessing curve depends on the attacker's guessing strategy and the distribution of user passwords both of which are unknown to us. In this work we aim to follow Kerckhoffs's principal and analyze the performance of an optimal attacker who knows the password distribution. Let λG denote the probability that such an attacker can crack a random user's password within G guesses. We develop several statistically rigorous techniques to upper and lower bound λG given N independent samples from the unknown password distribution ${\mathcal{P}}$. We show that our upper/lower bounds on λG hold with high confidence and we apply our techniques to analyze eight large password datasets. Our empirical analysis shows that even state-of-the-art password cracking models are often significantly less guess efficient than an attacker who can optimize its attack based on its (partial) knowledge of the password distribution. We also apply our statistical tools to re-examine different models of the password distribution i.e., the empirical password distribution and Zipf's Law. We find that the empirical distribution closely matches our upper/lower bounds on λG when the guessing number G is not too large i.e., G ≪ N. However, for larger values of G our empirical analysis rigorously demonstrates that the empirical distribution (resp. Zipf's Law) overestimates the attacker's success rate. We apply our statistical techniques to upper/lower bound the effectiveness of password throttling mechanisms (key-stretching) which are used to reduce the number of attacker guesses G. Finally, if we are willing to make an additional assumption about the way users respond to password restrictions, we can use our statistical techniques to evaluate the effectiveness of various password composition policies which restrict the passwords that users may select.