Sorry, you need to enable JavaScript to visit this website.

Improved Probabilistic Context-free Grammars for Passwords Using Word Extraction

Primary tabs

Citation Author(s):
Haibo Cheng, Wenting Li, Ping Wang, Kaitai Liang
Submitted by:
Haibo Cheng
Last updated:
22 June 2021 - 10:14pm
Document Type:
Presentation Slides
Document Year:
2021
Event:

Abstract 

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.

up
0 users have voted:

Dataset Files

ICASSP2021_slides.pdf

(19)

poster.pdf

(11)