HoneyGen: Generating Honeywords Using Representation Learning

Honeywords are false passwords injected in a database for detecting
password leakage. Generating honeywords is a challenging problem
due to the various assumptions about the adversary’s knowledge
as well as users’ password-selection behaviour. The success of a
Honeywords Generation Technique (HGT) lies on the resulting
honeywords; the method fails if an adversary can easily distinguish
the real password. In this paper, we propose HoneyGen, a practical
and highly robust HGT that produces realistic looking honeywords.
We do this by leveraging representation learning techniques to
learn useful and explanatory representations from a massive collection
of unstructured data, i.e., each operator’s password database.
We perform both a quantitative and qualitative evaluation of our
framework using the state-of-the-art metrics. Our results suggest
that HoneyGen generates high-quality honeywords that cause sophisticated
attackers to achieve low distinguishing success rates.