Modeling Password Guessing with Neural Networks
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چکیده
Passwords still dominate the authentication space, but they are vulnerable to many different attacks; in recent years, guessing attacks in particular have notably caused a few high-profile information leaks. Password strength checkers, which attempt to guard against guessing attacks by enforcing heuristics like length and character variety, only model resistance to these brute-force attacks by proxy. In this paper, we reproduce the work of Melicher et. al [1], designing a strength checker that directly bases its ratings on resistance to guessing attacks. We provide a qualitative comparison of the ratings output by this system to those of a popular heuristic one, zxcvbn.
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تاریخ انتشار 2017