PG-Pass: Targeted Online Password Guessing Model based on Pointer Generator Network

Yang Li, Yong Li, Xi Chen, Ruixin Shi, Jizhong Han

Abstract: Existing targeted online password guessing models were based on Probabilistic Context-Free Grammars (PCFG), having inherent disadvantages that guessing structures were always the same for different users. This problem would lead to poor guessing efficiency. In order to solve this problem, we propose a targeted online password guessing model (PG-Pass) composed of the pointer generator network. It could automatically learn the impact of personal information on passwords and guess the target user’s password more accurately. Through extensive experiments, we obtain the optimal parameters. The results show that with only Personally Identifiable Information (PII), the guessing success rate of the PG-Pass model could reach 19.49% in guessing once, which is ten times higher than TarGuess-I. When guessing 100 times, the guessing success rate could be 41.07%, proving the effectiveness of the proposed model in this paper.