Text passwords remain a primary means for user authentication on modern computer systems. However, recent studies have shown the promises of guessing user passwords efficiently with auxiliary information of the targeted accounts, such as the users' personal information, previously used passwords, or those used in other systems. Authentication rate-limiting mechanisms, such as account lockout and login throttling, are common methods to defeat online password cracking attacks. But to date, no published studies have investigated how authentication rate-limiting is implemented by popular websites. In this paper, we present a measurement study of such countermeasures against online password cracking. Towards this end, we propose a black-box approach to modeling and validating the websites' implementation of the rate-limiting mechanisms. We applied the tool to examine all 182 websites that we were able to analyze in the Alexa Top 500 websites in the United States. The results are rather surprising: 131 websites (72%) allow frequent, unsuccessful login attempts without account lockout or login throttling (though some of these websites force the adversary to lower the login frequency or constantly change his IP addresses to circumvent the rate-limiting enforcement). The remaining 51 websites are not absolutely secure either: 28 websites may block a legitimate user with correct passwords when the account is locked out, effectively enabling authentication denial-of-service attacks.
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Forgetting of Passwords: Ecological Theory and Data
It is well known that text-based passwords are hard to remember and that users prefer simple (and non-secure) passwords. However, despite extensive research on the topic, no principled account exists for explaining when a password will be forgotten. This paper contributes new data and a set of analyses building on the ecological theory of memory and forgetting. We propose that human memory naturally adapts according to an estimate of how often a password will be needed, such that often used, important passwords are less likely to be forgotten. We derive models for login duration and odds of recall as a function of rate of use and number of uses thus far. The models achieved a root-mean-square error (RMSE) of 1.8 seconds for login duration and 0.09 for recall odds for data collected in a month-long field experiment where frequency of password use was controlled. The theory and data shed new light on password management, account usage, password security and memorability.
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- NSF-PAR ID:
- 10091769
- Date Published:
- Journal Name:
- 27th USENIX Security Symposium (USENIX Security 2018)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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