| Paper ID | IFS-7.5 |
| Paper Title |
Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction |
| Authors |
Haibo Cheng, Wenting Li, Ping Wang, Peking University, China; Kaitai Liang, Delft University of Technology, China |
| Session | IFS-7: Information Hiding, Cryptography and Cybersecurity |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Information Forensics and Security: [USH] Usability And Human Factors |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 misestimation 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 our WordPCFG 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. |
