Wei Ou1,2,3, Shuai Liu1,*, Mengxue Pang1, Jianqiang Ma1, Qiuling Yue1, Wenbao Han1
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 463-490, 2025, DOI:10.32604/cmc.2025.063862
- 09 June 2025
Abstract TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing. Due to its remarkable guessing performance, the model has drawn considerable attention in password security research. However, through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information, we identified that the model fails to consider popular passwords and frequent substrings, and it uses overly broad personal information categories, with extensive duplicate statistics. To address these issues, we propose an improved password guessing model, TGI-FPR, which incorporates three semantic methods: (1) More >