When users choose passwords to secure their computers, data, or Internet service accounts, they tend to create passwords that are easy to remember. Probabilistic methods for password cracking profit from this fact, and allow the attackers and forensic investigators to guess user passwords more precisely. In this paper, we present our additions to a technique based on probabilistic context-free grammars. By modification of existing principles, we show how to guess more passwords for the same time, and how to reduce the total number of guesses without significant impact on success rate.
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