Published May 24, 2021 | Version Published
Conference paper Open

HoneyGen: Generating Honeywords Using Representation Learning

  • 1. University of Cyprus Nicosia, Cyprus
  • 2. CYENS Centre of Excellence Nicosia, Cyprus
  • 3. University of Cyprus Nicosia, Cyprus


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.


This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.



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SERUMS – Securing Medical Data in Smart Patient-Centric Healthcare Systems 826278
European Commission
REACT – REactively Defending against Advanced Cybersecurity Threats 786669
European Commission
RISE – Research Center on Interactive Media, Smart System and Emerging Technologies 739578
European Commission