Author(s): Guo, XZ (Guo, Xiaozhou); Tan, KJ (Tan, Kaijun); Liu, Y (Liu, Yi); Jin, M (Jin, Min); Lu, HX (Lu, Huaxiang)

Source: SENSORS Volume: 22 Issue: 12 Article Number: 4604 DOI: 10.3390/s22124604 Published: JUN 2022

Abstract: With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator-probabilistic context-free grammar (LPG-PCFG) based on PCFG. LPG-PCFG directionally increases the probability of low-probability passwords in the models' distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG-PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG-PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.

Accession Number: WOS:000817501700001

PubMed ID: 35746386

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Liu, Yi                  0000-0003-3056-7713

eISSN: 1424-8220

Full Text: https://www.mdpi.com/1424-8220/22/12/4604