Name
Abstract | ||
|---|---|---|
Probabilistic context-free grammars (PCFGs) have been proposed to capture password distributions, and further been used in password guessing attacks and password strength meters. However, current PCFGs suffer from the limitation of inaccurate segmentation of password, which leads to mis-estimation of password probability and thus seriously affects their performance. In this paper, we propose a word extraction approach for passwords, and further present an improved PCFG model, called WordPCFG. The WordPCFG using word extraction method can precisely extract semantic segments (called word) from passwords based on cohesion and freedom of words. We evaluate ourWordPCFG on six large-scale datasets, showing that WordPCFG cracks 83.04%-95.47% passwords and obtains 12.96%-71.84% improvement over the state-of-the-art PCFGs. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414886 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Word extraction, password, probabilistic context-free grammar | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haibo Cheng | 1 | 42 | 4.73 |
| Wenting Li | 2 | 29 | 8.58 |
| Ping Wang | 3 | 75 | 9.22 |
| Kaitai Liang | 4 | 612 | 45.13 |