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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Targeted online password guessing
T2 - CCS '16 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security
AU - Wang, Ding
AU - Zhang, Zijian
AU - Wang, Ping
AU - Yan, Jeff
AU - Huang, Xinyi
PY - 2016/10/24
Y1 - 2016/10/24
N2 - While trawling online/offline password guessing has been intensively studied, only a few studies have examined targeted online guessing, where an attacker guesses a specific victim's password for a service, by exploiting the victim's personal information such as one sister password leaked from her another account and some personally identifiable information (PII). A key challenge for targeted online guessing is to choose the most effective password candidates, while the number of guess attempts allowed by a server's lockout or throttling mechanisms is typically very small. We propose TarGuess, a framework that systematically characterizes typical targeted guessing scenarios with seven sound mathematical models, each of which is based on varied kinds of data available to an attacker. These models allow us to design novel and efficient guessing algorithms. Extensive experiments on 10 large real-world password datasets show the effectiveness of TarGuess. Particularly, TarGuess I~IV capture the four most representative scenarios and within 100 guesses: (1) TarGuess-I outperforms its foremost counterpart by 142% against security-savvy users and by 46% against normal users; (2) TarGuess-II outperforms its foremost counterpart by 169% on security-savvy users and by 72% against normal users; and (3) Both TarGuess-III and IV gain success rates over 73% against normal users and over 32% against security-savvy users. TarGuess-III and IV, for the first time, address the issue of cross-site online guessing when given the victim's one sister password and some PII.
AB - While trawling online/offline password guessing has been intensively studied, only a few studies have examined targeted online guessing, where an attacker guesses a specific victim's password for a service, by exploiting the victim's personal information such as one sister password leaked from her another account and some personally identifiable information (PII). A key challenge for targeted online guessing is to choose the most effective password candidates, while the number of guess attempts allowed by a server's lockout or throttling mechanisms is typically very small. We propose TarGuess, a framework that systematically characterizes typical targeted guessing scenarios with seven sound mathematical models, each of which is based on varied kinds of data available to an attacker. These models allow us to design novel and efficient guessing algorithms. Extensive experiments on 10 large real-world password datasets show the effectiveness of TarGuess. Particularly, TarGuess I~IV capture the four most representative scenarios and within 100 guesses: (1) TarGuess-I outperforms its foremost counterpart by 142% against security-savvy users and by 46% against normal users; (2) TarGuess-II outperforms its foremost counterpart by 169% on security-savvy users and by 72% against normal users; and (3) Both TarGuess-III and IV gain success rates over 73% against normal users and over 32% against security-savvy users. TarGuess-III and IV, for the first time, address the issue of cross-site online guessing when given the victim's one sister password and some PII.
U2 - 10.1145/2976749.2978339
DO - 10.1145/2976749.2978339
M3 - Conference contribution/Paper
SN - 9781450341394
SP - 1242
EP - 1254
BT - CCS '16 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security
PB - ACM
CY - New York
Y2 - 24 October 2016
ER -