3,754 research outputs found
Interpretable Probabilistic Password Strength Meters via Deep Learning
Probabilistic password strength meters have been proved to be the most
accurate tools to measure password strength. Unfortunately, by construction,
they are limited to solely produce an opaque security estimation that fails to
fully support the user during the password composition. In the present work, we
move the first steps towards cracking the intelligibility barrier of this
compelling class of meters. We show that probabilistic password meters
inherently own the capability of describing the latent relation occurring
between password strength and password structure. In our approach, the security
contribution of each character composing a password is disentangled and used to
provide explicit fine-grained feedback for the user. Furthermore, unlike
existing heuristic constructions, our method is free from any human bias, and,
more importantly, its feedback has a clear probabilistic interpretation. In our
contribution: (1) we formulate the theoretical foundations of interpretable
probabilistic password strength meters; (2) we describe how they can be
implemented via an efficient and lightweight deep learning framework suitable
for client-side operability.Comment: An abridged version of this paper appears in the proceedings of the
25th European Symposium on Research in Computer Security (ESORICS) 202
PassGAN: A Deep Learning Approach for Password Guessing
State-of-the-art password guessing tools, such as HashCat and John the
Ripper, enable users to check billions of passwords per second against password
hashes. In addition to performing straightforward dictionary attacks, these
tools can expand password dictionaries using password generation rules, such as
concatenation of words (e.g., "password123456") and leet speak (e.g.,
"password" becomes "p4s5w0rd"). Although these rules work well in practice,
expanding them to model further passwords is a laborious task that requires
specialized expertise. To address this issue, in this paper we introduce
PassGAN, a novel approach that replaces human-generated password rules with
theory-grounded machine learning algorithms. Instead of relying on manual
password analysis, PassGAN uses a Generative Adversarial Network (GAN) to
autonomously learn the distribution of real passwords from actual password
leaks, and to generate high-quality password guesses. Our experiments show that
this approach is very promising. When we evaluated PassGAN on two large
password datasets, we were able to surpass rule-based and state-of-the-art
machine learning password guessing tools. However, in contrast with the other
tools, PassGAN achieved this result without any a-priori knowledge on passwords
or common password structures. Additionally, when we combined the output of
PassGAN with the output of HashCat, we were able to match 51%-73% more
passwords than with HashCat alone. This is remarkable, because it shows that
PassGAN can autonomously extract a considerable number of password properties
that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS
2018 Workshop on Security in Machine Learning (SecML'18), see
https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd
Towards Human Computable Passwords
An interesting challenge for the cryptography community is to design
authentication protocols that are so simple that a human can execute them
without relying on a fully trusted computer. We propose several candidate
authentication protocols for a setting in which the human user can only receive
assistance from a semi-trusted computer --- a computer that stores information
and performs computations correctly but does not provide confidentiality. Our
schemes use a semi-trusted computer to store and display public challenges
. The human user memorizes a random secret mapping
and authenticates by computing responses
to a sequence of public challenges where
is a function that is easy for the
human to evaluate. We prove that any statistical adversary needs to sample
challenge-response pairs to recover , for
a security parameter that depends on two key properties of . To
obtain our results, we apply the general hypercontractivity theorem to lower
bound the statistical dimension of the distribution over challenge-response
pairs induced by and . Our lower bounds apply to arbitrary
functions (not just to functions that are easy for a human to evaluate),
and generalize recent results of Feldman et al. As an application, we propose a
family of human computable password functions in which the user
needs to perform primitive operations (e.g., adding two digits or
remembering ), and we show that .
For these schemes, we prove that forging passwords is equivalent to recovering
the secret mapping. Thus, our human computable password schemes can maintain
strong security guarantees even after an adversary has observed the user login
to many different accounts.Comment: Fixed bug in definition of Q^{f,j} and modified proofs accordingl
Exploring the Impact of Password Dataset Distribution on Guessing
Leaks from password datasets are a regular occurrence. An organization may
defend a leak with reassurances that just a small subset of passwords were
taken. In this paper we show that the leak of a relatively small number of
text-based passwords from an organizations' stored dataset can lead to a
further large collection of users being compromised. Taking a sample of
passwords from a given dataset of passwords we exploit the knowledge we gain of
the distribution to guess other samples from the same dataset. We show
theoretically and empirically that the distribution of passwords in the sample
follows the same distribution as the passwords in the whole dataset. We propose
a function that measures the ability of one distribution to estimate another.
Leveraging this we show that a sample of passwords leaked from a given dataset,
will compromise the remaining passwords in that dataset better than a sample
leaked from another source
GOTCHA Password Hackers!
We introduce GOTCHAs (Generating panOptic Turing Tests to Tell Computers and
Humans Apart) as a way of preventing automated offline dictionary attacks
against user selected passwords. A GOTCHA is a randomized puzzle generation
protocol, which involves interaction between a computer and a human.
Informally, a GOTCHA should satisfy two key properties: (1) The puzzles are
easy for the human to solve. (2) The puzzles are hard for a computer to solve
even if it has the random bits used by the computer to generate the final
puzzle --- unlike a CAPTCHA. Our main theorem demonstrates that GOTCHAs can be
used to mitigate the threat of offline dictionary attacks against passwords by
ensuring that a password cracker must receive constant feedback from a human
being while mounting an attack. Finally, we provide a candidate construction of
GOTCHAs based on Inkblot images. Our construction relies on the usability
assumption that users can recognize the phrases that they originally used to
describe each Inkblot image --- a much weaker usability assumption than
previous password systems based on Inkblots which required users to recall
their phrase exactly. We conduct a user study to evaluate the usability of our
GOTCHA construction. We also generate a GOTCHA challenge where we encourage
artificial intelligence and security researchers to try to crack several
passwords protected with our scheme.Comment: 2013 ACM Workshop on Artificial Intelligence and Security (AISec
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