Abstract
Human chosen passwords are often predictable. Research has shown that users of similar demographics or choosing passwords for the same website will often choose similar passwords. This knowledge is leveraged by human password guessers who use it to tailor their attacks. In this paper, we demonstrate that a learning algorithm can actively learn these same characteristics of the passwords as it is guessing and that it can leverage this information to adaptively improve its guessing. Furthermore, we show that if we split our candidate wordlists based on these characteristics, then a multi-armed bandit style guessing algorithm can adaptively choose to guess from the wordlist which will maximise successes.
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