PointerGuess: Targeted Password Guessing Model Using Pointer Mechanism

Most existing targeted password guessing models view users' reuse behaviors as sequences of edit operations (e.g., insert and delete) performed on old passwords. These atomic edit operations are limited to modifying one character at a time and cannot fully cover users' complex password modification behaviors (e.g., modifying the password structure). This partially leads to a significant gap between the proportion of users' reused passwords and the success rates that existing targeted password models can achieve. To fill this gap, this paper models users' reuse behaviors by focusing on two key components: (1) What they want to copy/keep; (2) What they want to tweak. More specifically, we introduce the pointer mechanism and propose a new targeted guessing model, namely POINT-ERGUESS. By hierarchically redefining password reuse from both personal and population-wide perspectives, we can accurately and comprehensively characterize users' password reuse behaviors. Moreover, we propose MS-POINTERGUESS, which can employ the victim's multiple leaked passwords. By employing 13 large-scale real-world password datasets, we demonstrate that POINTERGUESS is effective: (1) When the victim's password at site A (namely pw A ) is known, within 100 guesses, the average success rate of POINTERGUESS in guessing her password at site B (namely pw B , pw A = pw B ) is 25.21% (for common users) and 12.34% (for security-savvy users), respectively, which is 21.23%∼71.54% (38.37% on average) higher than its foremost counterparts; (2) When not excluding identical password pairs (i.e., pw A can equal pw B ), within 100 guesses, the average success rate of POINT-ERGUESS is 48.30% (for common users) and 28.42% (for security-savvy users), respectively, which is 6.31%∼15.92% higher than its foremost counterparts; (3) Within 100 guesses, the MS-POINTERGUESS further improves the cracking success rate by 31.21% compared to POINTERGUESS.