157 research outputs found
A Survey on Password Guessing
Text password has served as the most popular method for user authentication
so far, and is not likely to be totally replaced in foreseeable future.
Password authentication offers several desirable properties (e.g., low-cost,
highly available, easy-to-implement, reusable). However, it suffers from a
critical security issue mainly caused by the inability to memorize complicated
strings of humans. Users tend to choose easy-to-remember passwords which are
not uniformly distributed in the key space. Thus, user-selected passwords are
susceptible to guessing attacks. In order to encourage and support users to use
strong passwords, it is necessary to simulate automated password guessing
methods to determine the passwords' strength and identify weak passwords. A
large number of password guessing models have been proposed in the literature.
However, little attention was paid to the task of providing a systematic survey
which is necessary to review the state-of-the-art approaches, identify gaps,
and avoid duplicate studies. Motivated by that, we conduct a comprehensive
survey on all password guessing studies presented in the literature from 1979
to 2022. We propose a generic methodology map to present an overview of
existing methods. Then, we explain each representative approach in detail. The
experimental procedures and available datasets used to evaluate password
guessing models are summarized, and the reported performances of representative
studies are compared. Finally, the current limitations and the open problems as
future research directions are discussed. We believe that this survey is
helpful to both experts and newcomers who are interested in password securityComment: 35 pages, 5 figures, 5 table
CharBot: A Simple and Effective Method for Evading DGA Classifiers
Domain generation algorithms (DGAs) are commonly leveraged by malware to
create lists of domain names which can be used for command and control (C&C)
purposes. Approaches based on machine learning have recently been developed to
automatically detect generated domain names in real-time. In this work, we
present a novel DGA called CharBot which is capable of producing large numbers
of unregistered domain names that are not detected by state-of-the-art
classifiers for real-time detection of DGAs, including the recently published
methods FANCI (a random forest based on human-engineered features) and LSTM.MI
(a deep learning approach). CharBot is very simple, effective and requires no
knowledge of the targeted DGA classifiers. We show that retraining the
classifiers on CharBot samples is not a viable defense strategy. We believe
these findings show that DGA classifiers are inherently vulnerable to
adversarial attacks if they rely only on the domain name string to make a
decision. Designing a robust DGA classifier may, therefore, necessitate the use
of additional information besides the domain name alone. To the best of our
knowledge, CharBot is the simplest and most efficient black-box adversarial
attack against DGA classifiers proposed to date
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
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
Tiresias: Predicting Security Events Through Deep Learning
With the increased complexity of modern computer attacks, there is a need for
defenders not only to detect malicious activity as it happens, but also to
predict the specific steps that will be taken by an adversary when performing
an attack. However this is still an open research problem, and previous
research in predicting malicious events only looked at binary outcomes (e.g.,
whether an attack would happen or not), but not at the specific steps that an
attacker would undertake. To fill this gap we present Tiresias, a system that
leverages Recurrent Neural Networks (RNNs) to predict future events on a
machine, based on previous observations. We test Tiresias on a dataset of 3.4
billion security events collected from a commercial intrusion prevention
system, and show that our approach is effective in predicting the next event
that will occur on a machine with a precision of up to 0.93. We also show that
the models learned by Tiresias are reasonably stable over time, and provide a
mechanism that can identify sudden drops in precision and trigger a retraining
of the system. Finally, we show that the long-term memory typical of RNNs is
key in performing event prediction, rendering simpler methods not up to the
task
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data
We develop the first universal password model -- a password model that, once
pre-trained, can automatically adapt to any password distribution. To achieve
this result, the model does not need to access any plaintext passwords from the
target set. Instead, it exploits users' auxiliary information, such as email
addresses, as a proxy signal to predict the underlying target password
distribution. The model uses deep learning to capture the correlation between
the auxiliary data of a group of users (e.g., users of a web application) and
their passwords. It then exploits those patterns to create a tailored password
model for the target community at inference time. No further training steps,
targeted data collection, or prior knowledge of the community's password
distribution is required. Besides defining a new state-of-the-art for password
strength estimation, our model enables any end-user (e.g., system
administrators) to autonomously generate tailored password models for their
systems without the often unworkable requirement of collecting suitable
training data and fitting the underlying password model. Ultimately, our
framework enables the democratization of well-calibrated password models to the
community, addressing a major challenge in the deployment of password security
solutions on a large scale.Comment: v0.0
The Emerging Threat of Ai-driven Cyber Attacks: A Review
Cyberattacks are becoming more sophisticated and ubiquitous. Cybercriminals are inevitably adopting Artificial Intelligence (AI) techniques to evade the cyberspace and cause greater damages without being noticed. Researchers in cybersecurity domain have not researched the concept behind AI-powered cyberattacks enough to understand the level of sophistication this type of attack possesses. This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious used of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks. Searches in ACM, arXiv Blackhat, Scopus, Springer, MDPI, IEEE Xplore and other sources were executed to retrieve relevant articles. Out of the 936 papers that met our search criteria, a total of 46 articles were finally selected for this study. The result shows that 56% of the AI-Driven cyberattack technique identified was demonstrated in the access and penetration phase, 12% was demonstrated in exploitation, and command and control phase, respectively; 11% was demonstrated in the reconnaissance phase; 9% was demonstrated in the delivery phase of the cybersecurity kill chain. The findings in this study shows that existing cyber defence infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cybersecurity infrastructures to combat these emerging threats.publishedVersio
CharBot : a simple and effective method for evading DGA classifiers
Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names, which can be used for command and control (C&C) purposes. Approaches based on machine learning have recently been developed to automatically detect generated domain names in real-time. In this paper, we present a novel DGA called CharBot, which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of the DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM.MI (a deep learning approach). The CharBot is very simple, effective, and requires no knowledge of the targeted DGA classifiers. We show that retraining the classifiers on CharBot samples is not a viable defense strategy. We believe these findings show that DGA classifiers are inherently vulnerable to adversarial attacks if they rely only on the domain name string to make a decision. Designing a robust DGA classifier may, therefore, necessitate the use of additional information besides the domain name alone. To the best of our knowledge, the CharBot is the simplest and most efficient black-box adversarial attack against DGA classifiers proposed to date
- …