Search-based Ordered Password Generation of Autoregressive Neural Networks
CoRR(2024)
摘要
Passwords are the most widely used method of authentication and password
guessing is the essential part of password cracking and password security
research. The progress of deep learning technology provides a promising way to
improve the efficiency of password guessing. However, current research on
neural network password guessing methods mostly focuses on model structure and
has overlooked the generation method. Due to the randomness of sampling, not
only the generated passwords have a large number of duplicates, but also the
order in which passwords generated is random, leading to inefficient password
attacks. In this paper, we propose SOPG, a search-based ordered password
generation method, which enables the password guessing model based on
autoregressive neural network to generate passwords in approximately descending
order of probability. Experiment on comparison of SOPG and Random sampling
shows passwords generated by SOPG do not repeat, and when they reach the same
cover rate, SOPG requires fewer inferences and far fewer generated passwords
than Random sampling, which brings great efficiency improvement to subsequent
password attacks. We build SOPGesGPT, a password guessing model based on GPT,
using SOPG to generate passwords. Compared with the most influential models
OMEN, FLA, PassGAN, VAEPass and the latest model PassGPT in one-site test,
experiments show that SOPGesGPT is far ahead in terms of both effective rate
and cover rate. As to cover rate that everyone recognizes, SOPGesGPT reaches
35.06
VAEPass, and PassGPT respectively.
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