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2021 | OriginalPaper | Buchkapitel

Advances in Password Recovery Using Generative Deep Learning Techniques

verfasst von: David Biesner, Kostadin Cvejoski, Bogdan Georgiev, Rafet Sifa, Erik Krupicka

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2021

Verlag: Springer International Publishing

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Abstract

Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates. In the present work we study a broad collection of deep learning and probabilistic based models in the light of password guessing: attention-based deep neural networks, autoencoding mechanisms and generative adversarial networks. We provide novel generative deep-learning models in terms of variational autoencoders exhibiting state-of-art sampling performance, yielding additional latent-space features such as interpolations and targeted sampling. Lastly, we perform a thorough empirical analysis in a unified controlled framework over well-known datasets (RockYou, LinkedIn, MySpace, Youku, Zomato, Pwnd). Our results not only identify the most promising schemes driven by deep neural networks, but also illustrate the strengths of each approach in terms of generation variability and sample uniqueness.

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Metadaten
Titel
Advances in Password Recovery Using Generative Deep Learning Techniques
verfasst von
David Biesner
Kostadin Cvejoski
Bogdan Georgiev
Rafet Sifa
Erik Krupicka
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-86365-4_2

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