Generative Deep Learning Techniques for Password Generation
This work addresses the problem of generating realistic password candidates for password guessing, which is important for cybersecurity researchers and practitioners to understand password vulnerabilities.
This paper investigates various deep learning and probabilistic models for password generation, including attention-based networks, autoencoders, and generative adversarial networks. They introduce novel variational autoencoders that achieve state-of-the-art sampling performance and offer latent-space features like interpolations and targeted sampling.
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, 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.