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Message-ID: <20160818163024.GA6515@openwall.com>
Date: Thu, 18 Aug 2016 19:30:24 +0300
From: Solar Designer <solar@...nwall.com>
To: john-users@...ts.openwall.com
Subject: neural networks
Hi,
This is not an end-user topic yet, because there's no end-user usable
code yet, and there might not ever be. But I felt this is of interest
to the JtR user community anyway, and as we do not dive into source code
details yet it is not a topic for john-dev yet.
There's interesting new work here:
"Code for cracking passwords with neural networks"
https://github.com/cupslab/neural_network_cracking
Paper/slides:
https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/melicher
The authors include a comparison against JtR and hashcat, but without
detail on which versions and modes were used. (I am guessing JtR's
Markov mode was, but incremental mode was not. That's unfortunate.)
I only skimmed the paper so far. In one place, it mentions needing 16
days to generate 10^10 candidate passwords on a GPU. This would make
the approach usable for attacking (semi-)slow hashes, but not fast ones.
I am not convinced there's an improvement over Markov and incremental
modes here - need independent testing for that - but maybe this is a
mode that would be reasonable to have alongside other modes we have.
Alexander
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