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Password Exhaustion: Predicting the End of Password Usefulness

Authors: Luke St. Clair; Lisa Johansen; William Enck; Matthew Pirretti; Patrick Traynor; Patrick D. McDaniel; Trent Jaeger;

Password Exhaustion: Predicting the End of Password Usefulness

Abstract

Passwords are currently the dominant authentication mechanism in computing systems. However, users are unwilling or unable to retain passwords with a large amount of entropy. This reality is exacerbated by the increasing ability of systems to mount offline attacks. In this paper, we evaluate the degree to which the previous statements are true and attempt to ascertain the point at which passwords are no longer sufficient to securely mediate authentication. In order to demonstrate this, we develop an analytical model for computation to understand the time required to recover random passwords. Further, an empirical study suggests the situation is much worse. In fact, we found that past systems vulnerable to offline attacks will be obsolete in 5-15 years, and our study suggests that a large number of these systems are already obsolete. We conclude that we must discard or fundamentally change these systems, and to that effect, we suggest a number of ways to prevent offline attacks.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
25
Top 10%
Top 10%
Average