HoneyGen: Generating Honeywords Using Representation Learning

Open Access

Abstract

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.