SE#PCFG: Semantically Enhanced PCFG for Password Analysis and Cracking
Yangde Wang Weidong Qiu Weicheng Zhang Hao Tian Shujun Li
Much research has been done on user-generated textual passwords.
Surprisingly, semantic information in such passwords remain underinvestigated,
with passwords created by English- and/or Chinese-speaking users being more
studied with limited semantics. This paper fills this gap by proposing a
general framework based on semantically enhanced PCFG (probabilistic
context-free grammars) named SE#PCFG. It allowed us to consider 43 types of
semantic information, the richest set considered so far, for semantic password
analysis. Applying SE#PCFG to 17 large leaked password databases of user
speaking four languages (English, Chinese, German and French), we demonstrate
its usefulness and report a wide range of new insights about password semantics
at different levels such as cross-website password correlations. Furthermore,
based on SE#PCFG and a new systematic smoothing method, we proposed the
Semantically Enhanced Password Cracking Architecture (SEPCA). To compare the
performance of SEPCA against three state-of-the-art (SOTA) benchmarks in terms
of the password coverage rate: two other PCFG variants and FLA. Our
experimental results showed that SEPCA outperformed all the three benchmarks
consistently and significantly across 52 test cases, by up to 21.53%, 52.55%
and 7.86%, respectively, at the user level (with duplicate passwords). At the
level of unique passwords, SEPCA also beats the three benchmarks by up to
33.32%, 86.19% and 10.46%, respectively. The results demonstrated the power of
SEPCA as a new password cracking framework.