A machine learning technique can make computers up to 24% better at guessing passwords, according to researchers at the New York Institute of Technology and the Stevens Institute of Technology.
The new technique, called PassGAN, relies on a generative adversarial network (GAN) and was able to generate password matches that were not produced by any established password rules.
The team combined the output of PassGAN with the output of the HashCat password recovery utility, and was able to match between 18% and 24% more unique passwords compared to HashCat alone.
In contrast with traditional password generation rules, PassGAN can generate a practically unbounded number of password guesses.
The experiments demonstrated that the number of new, unique password guesses increases steadily with the overall number of passwords generated by the GAN.
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Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA
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