An Implementation and Evaluation of PDF Password Cracking Using John the Ripper And Crunch

Abstract

It can be challenging to choose the most effective wordmangling rules to apply while undertaking a dictionary-based
password cracking attempt. We discuss a new method for
generating password structures in the highest possibility order in
this work. Based on a training set of previously revealed passwords,
we first build an artificial probabilistic context-free grammar. As a
result of this grammar, we can generate word-mangling rules and,
as a result, password guesses for password cracking. By putting our
tools and strategies to the test on genuine password sets, we will
show that this strategy appears to be a more effective way to crack
passwords than traditional methods. Our approach cracked 28
percent to 129 percent more passwords than John the Ripper, a
publicly available standard password cracking software, in one set
of testing. We'll construct a wordlist for dictionary attack using the
Crunch tool.