@inproceedings{6cda1a99a0524e22b06ba1bdf1a79d76,
title = "Improving password guessing via representation learning",
abstract = "Learning useful representations from unstructured data is one of the core challenges, as well as a driving force, of modern data-driven approaches. Deep learning has demonstrated the broad advantages of learning and harnessing such representations.In this paper, we introduce a deep generative model representation learning approach for password guessing. We show that an abstract password representation naturally offers compelling and versatile properties that open new directions in the extensively studied, and yet presently active, password guessing field. These properties can establish novel password generation techniques that are neither feasible nor practical with the existing probabilistic and non-probabilistic approaches. Based on these properties, we introduce: (1) A general framework for conditional password guessing that can generate passwords with arbitrary biases; and (2) an Expectation Maximization-inspired framework that can dynamically adapt the estimated password distribution to match the distribution of the attacked password set.",
keywords = "Deep-learning, Password-Security",
author = "Dario Pasquini and Ankit Gangwal and Giuseppe Ateniese and Massimo Bernaschi and Mauro Conti",
year = "2021",
doi = "10.1109/SP40001.2021.00016",
language = "English",
series = "Proceedings - IEEE Symposium on Security and Privacy",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "1382--1399",
booktitle = "Proceedings - 2021 IEEE Symposium on Security and Privacy, SP 2021",
address = "United States",
note = "42nd IEEE Symposium on Security and Privacy, SP 2021 ; Conference date: 24-05-2021 Through 27-05-2021",
}