Generative Deep Learning Techniques for Password Generation

Autor:	Biesner, David, Cvejoski, Kostadin, Georgiev, Bogdan, Sifa, Rafet, Krupicka, Erik
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	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. Comment: 25 pages, 13 figures. Comments welcome!
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.05685 Zobrazit plný text záznamu View this record from Arxiv