6 accepted papers
The robustness of neural networks is crucial in safety-critical applications, where identifying a reliable input space is essential for effective model selection, robustness evaluation, and the development of reliable control strategies. Most existing robustness verification methods assess the worst…
With the increasing prevalence of personal information breaches, targeted password guessing based on user-specific data has emerged as a serious security threat. Existing targeted password guessing attacks primarily rely on traditional statistical language models, which have limited capability in ad…
Passwords are widely used for website authentication, but they are vulnerable to guessing attacks. To measure password guessability, the commonly used approach involves modeling the distribution of passwords with a password probability model and then estimating the guessability using Monte Carlo met…
Modeling password distributions is a fundamental problem in password security, benefiting the research and applications on password guessing, password strength meters, honey password vaults, etc. As one of the best segment-based password models, WordPCFG has been proposed to capture individual seman…
Probabilistic context-free grammars (PCFGs) have been pro-posed to capture password distributions, and further been used in password guessing attacks and password strength meters. However, current PCFGs suffer from the limitation of inaccurate segmentation of password, which leads to misestimation o…
We consider the problem of variance reduction in randomized controlled trials, through the use of covariates correlated with the outcome but independent of the treatment. We propose a machine learning regression-adjusted treatment effect estimator, which we call MLRATE. MLRATE uses machine learning…