Abstract
Honeywords are fictitious passwords inserted into databases in order to identify password breaches. Producing honeywords that are difficult to distinguish from actual passwords automatically is a sophisticated task. We propose a honeyword generation technique (HGT) called HoneyGAN and an evaluation metric based on representation learning for measuring the indistinguishability of fake passwords, together with a novel attack model for evaluating the efficiency of HGTs. We compare HoneyGAN to state-of-the-art HGTs proposed in the literature using both evaluation metrics and a human study. Our findings indicate that HoneyGAN creates genuine-looking honeywords, leading to a low success rate for knowledgeable attackers in identifying them. We also demonstrate that our attack model is more capable of finding real passwords among sets of honeywords compared to previous works.
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Acknowledgement
The authors acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), funding reference number RGPIN-2018-05919.
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Yu, F., Vargas Martin, M. (2023). HoneyGAN: Creating Indistinguishable Honeywords with Improved Generative Adversarial Networks. In: Lenzini, G., Meng, W. (eds) Security and Trust Management. STM 2022. Lecture Notes in Computer Science, vol 13867. Springer, Cham. https://doi.org/10.1007/978-3-031-29504-1_11
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DOI: https://doi.org/10.1007/978-3-031-29504-1_11
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