TY - GEN
T1 - Password Cracking by Exploiting User Group Information
AU - Zhou, Beibei
AU - He, Daojing
AU - Zhu, Sencun
AU - Zhu, Shanshan
AU - Chan, Sammy
AU - Yang, Xiao
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - The past research study on the characteristics of passwords has paid much attention to language, regional or cultural differences and usability. However, few studies have pointed out differences due to information such as application types, users’ occupations, religious beliefs, and meanings of the digits in the culture. In this article, for the first time we put forward the concept of “group” characteristics, and found that the passwords of different groups have obviously different characteristics. For example, when dividing groups by religions of users, Christian groups like to include biblical names and words in passwords, such as “jesus”, “christ”, “angels” and “faith”. Accordingly, we propose gPGM, a neural network-based password guessing method that leverages group information to increase attack success. Our experiments show that gPGM can significantly increase the password cracking rate. In addition, the cracking rates for different groups, under the same number of guesses, also vary. For example, the cracking rate of the game group is very high, but that of the hacker group is very low.
AB - The past research study on the characteristics of passwords has paid much attention to language, regional or cultural differences and usability. However, few studies have pointed out differences due to information such as application types, users’ occupations, religious beliefs, and meanings of the digits in the culture. In this article, for the first time we put forward the concept of “group” characteristics, and found that the passwords of different groups have obviously different characteristics. For example, when dividing groups by religions of users, Christian groups like to include biblical names and words in passwords, such as “jesus”, “christ”, “angels” and “faith”. Accordingly, we propose gPGM, a neural network-based password guessing method that leverages group information to increase attack success. Our experiments show that gPGM can significantly increase the password cracking rate. In addition, the cracking rates for different groups, under the same number of guesses, also vary. For example, the cracking rate of the game group is very high, but that of the hacker group is very low.
KW - Group password
KW - Password analysis
KW - Password attack
UR - https://www.scopus.com/pages/publications/85207539780
U2 - 10.1007/978-3-031-64948-6_26
DO - 10.1007/978-3-031-64948-6_26
M3 - 会议稿件
AN - SCOPUS:85207539780
SN - 9783031649479
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 514
EP - 532
BT - Security and Privacy in Communication Networks - 19th EAI International Conference, SecureComm 2023, Proceedings
A2 - Duan, Haixin
A2 - Debbabi, Mourad
A2 - de Carné de Carnavalet, Xavier
A2 - Luo, Xiapu
A2 - Au, Man Ho Allen
A2 - Du, Xiaojiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2023
Y2 - 19 October 2023 through 21 October 2023
ER -