TransPCFG: Transferring the Grammars from Short Passwords to Guess Long Passwords Effectively

Long passwords are gaining popularity in password policy recommendations; however, data-driven guessing studies are woefully inadequate in adapting to long passwords, lacking in both guessing efficiency and their composition guidelines. For state-of-the-art data-driven password guessing methods such as PCFGs (Probabilistic Context-free Grammars), their guessing efficiency is limited by the presence of a large scale training data, or the lack thereof. Given that long passwords leaked in the real world are typically scarce, coupled with the fact that the data-driven methods’ performance depends on training data, obtaining good performance on long passwords has become a key challenge. To overcome the dataset limitation, we propose a framework TransPCFG , that transfers the knowledge, (i.e., grammars in PCFGs), from short passwords to facilitate long password guessing. We further perform an empirical evaluation based on three real-world datasets and the results demonstrate superior performance over the state-of-the-art data-driven guessing methods under ${10}^{14}$ offline guesses. For passwords with 16 characters, TransPCFG can compromise an average of 23.30% of the passwords, outperforming PCFG_v4.1 by 56.10%. Additionally,for better password-composition guidelines, we find that long password-composition policies requiring more segments are more resistant to guessing attacks. For the segment, the password 12zxcvbnword1997 has four segments since it follows the template ${Digit}_{2}{Keyboard}_{6}{Letter}_{4}{Year}_{4}$ . We thus recommend users to create long passwords with four or more segments instead of the widely recommended more character classes for security.