Next Gen PCFG Password Cracking
Shiva Houshmand, Sudhir Aggarwal, Randy Flood · IEEE Transactions on Information Forensics and Security · 2015
Passwords continue to remain an important authentication technique. The probabilistic context-free grammar-based password cracking system of Weir et al. was an important addition to dictionary-based password cracking approaches. In this paper, we show how to substantially improve upon this system by systematically adding keyboard patterns and multiword patterns (two or more words in the alphabetic part of a password) to the context-free grammars used in the probabilistic password cracking. Our results on cracking multiple data sets show that by learning these new classes of patterns, we can achieve up to 22% improvement over the original system. In this paper, we also define metrics to help analyze and improve attack dictionaries. Using our approach to improving the dictionary, we achieve an additional improvement of ~33% by increasing the coverage of a standard attack dictionary. Combining both approaches, we can achieve a 55% improvement over the previous system. Our tests were done over fairly long password guessing sessions (up to 85 billion) and thus show the uniform effectiveness of our techniques for long cracking sessions.