GNPassGAN: Improved Generative Adversarial Networks For Trawling Offline Password Guessing

Autor:	Yu, Fangyi, Martin, Miguel Vargas
Rok vydání:	2022
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence I.2
Zdroj:	2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), 2022, pp. 10-18
Druh dokumentu:	Working Paper
DOI:	10.1109/EuroSPW55150.2022.00009
Popis:	The security of passwords depends on a thorough understanding of the strategies used by attackers. Unfortunately, real-world adversaries use pragmatic guessing tactics like dictionary attacks, which are difficult to simulate in password security research. Dictionary attacks must be carefully configured and modified to represent an actual threat. This approach, however, needs domain-specific knowledge and expertise that are difficult to duplicate. This paper reviews various deep learning-based password guessing approaches that do not require domain knowledge or assumptions about users' password structures and combinations. It also introduces GNPassGAN, a password guessing tool built on generative adversarial networks for trawling offline attacks. In comparison to the state-of-the-art PassGAN model, GNPassGAN is capable of guessing 88.03\% more passwords and generating 31.69\% fewer duplicates. Comment: 9 pages, 8 tables, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.06943 Zobrazit plný text záznamu View this record from Arxiv