Password Guessing Using Random Forest

Passwords are the most widely used authentication method, and guessing attacks are the most effective method for password strength evaluation. However, existing password guessing models are generally built on traditional statistics or deep learning, and there has been no research on password guessing that employs classical machine learning. To fill this gap, this paper provides a brand new technical route for password guessing. More specifically, we re-encode the password characters and make it possible for a series of classical machine learning techniques that tackle multiclass classification problems (such as random forest, boosting algorithms and their variants) to be used for password guessing. Further, we propose RFGuess, a random-forest based framework that characterizes the three most representative password guessing scenarios (i.e., trawling guessing, targeted guessing based on personally identifiable information (PII) and on users' password reuse behaviors). Besides its theoretical significance, this work is also of practical value. Experiments using 13 large real-world password datasets demonstrate that our random-forest based guessing models are effective: (1) RFGuess for trawling guessing scenarios, whose guessing success rates are comparable to its foremost counterparts; (2) RFGuess-PII for targeted guessing based on PII, which guesses 20%∼28% of common users within 100 guesses, outperforming its foremost counterpart by 7%∼13%; (3) RFGuess-Reuse for targeted guessing based on users' password reuse/modification behaviors, which performs the best or 2nd best among related models. We believe this work makes a substantial step toward introducing classical machine learning techniques into password guessing.