644 research outputs found
Bernoulli honeywords
Decoy passwords, or ``honeywords,'' planted in a credential database can
alert a site to its breach if ever submitted in a login attempt. To be
effective, some honeywords must appear at least as likely to be user-chosen
passwords as the real ones, and honeywords must be very difficult to guess
without having breached the database, to prevent false breach alarms. These
goals have proved elusive, however, for heuristic honeyword generation
algorithms. In this paper we explore an alternative strategy in which the
defender treats honeyword selection as a Bernoulli process in which each
possible password (except the user-chosen one) is selected as a honeyword
independently with some fixed probability. We show how Bernoulli honeywords can
be integrated into two existing system designs for leveraging honeywords: one
based on a honeychecker that stores the secret index of the user-chosen
password in the list of account passwords, and another that does not leverage
secret state at all. We show that Bernoulli honeywords enable analytic
derivation of false breach-detection probabilities irrespective of what
information the attacker gathers about the sites' users; that their true and
false breach-detection probabilities demonstrate compelling efficacy; and that
Bernoulli honeywords can even enable performance improvements in modern
honeyword system designs
The Impact of Exposed Passwords on Honeyword Efficacy
Honeywords are decoy passwords that can be added to a credential database; if
a login attempt uses a honeyword, this indicates that the site's credential
database has been leaked. In this paper we explore the basic requirements for
honeywords to be effective, in a threat model where the attacker knows
passwords for the same users at other sites. First, we show that for
user-chosen (vs. algorithmically generated, i.e., by a password manager)
passwords, existing honeyword-generation algorithms largely fail to achieve
reasonable tradeoffs between false positives and false negatives in this threat
model. Second, we show that for users leveraging algorithmically generated
passwords, state-of-the-art methods for honeyword generation will produce
honeywords that are not sufficiently deceptive, yielding many false negatives.
Instead, we find that only a honeyword-generation algorithm that uses the same
password generator as the user can provide deceptive honeywords in this case.
However, when the defender's ability to infer the generator from the (one)
account password is less accurate than the attacker's ability to infer the
generator from potentially many, this deception can again wane. Taken together,
our results provide a cautionary note for the state of honeyword research and
pose new challenges to the field
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