547 research outputs found
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
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
Cybersecurity: Past, Present and Future
The digital transformation has created a new digital space known as
cyberspace. This new cyberspace has improved the workings of businesses,
organizations, governments, society as a whole, and day to day life of an
individual. With these improvements come new challenges, and one of the main
challenges is security. The security of the new cyberspace is called
cybersecurity. Cyberspace has created new technologies and environments such as
cloud computing, smart devices, IoTs, and several others. To keep pace with
these advancements in cyber technologies there is a need to expand research and
develop new cybersecurity methods and tools to secure these domains and
environments. This book is an effort to introduce the reader to the field of
cybersecurity, highlight current issues and challenges, and provide future
directions to mitigate or resolve them. The main specializations of
cybersecurity covered in this book are software security, hardware security,
the evolution of malware, biometrics, cyber intelligence, and cyber forensics.
We must learn from the past, evolve our present and improve the future. Based
on this objective, the book covers the past, present, and future of these main
specializations of cybersecurity. The book also examines the upcoming areas of
research in cyber intelligence, such as hybrid augmented and explainable
artificial intelligence (AI). Human and AI collaboration can significantly
increase the performance of a cybersecurity system. Interpreting and explaining
machine learning models, i.e., explainable AI is an emerging field of study and
has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-
Advances in Information Security and Privacy
With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue
Enabling Quantum Cybersecurity Analytics in Botnet Detection: Stable Architecture and Speed-up through Tree Algorithms
For the first time, we enable the execution of hybrid machine learning
methods on real quantum computers, with 100 data samples, and also with
real-device-based simulations, with 5,000 data samples and thereby
outperforming the current state of research of Suryotrisongko and Musashi from
the year 2022 who were dealing with 1,000 data samples and not with simulations
on quantum real devices but on quantum simulators (i.e. pure software-based
emulators) only. Additionally, we beat their reported accuracy of 76.8% by an
average accuracy of 89.0%, all of this in a total computation time of 382
seconds only. They did not report the execution time. We gain this significant
progress by a two-fold strategy: First, we provide a stabilized quantum
architecture that enables us to execute HQML algorithms on real quantum
devices. Second, we design a new form of hybrid quantum binary classification
algorithms that are based on Hoeffding decision tree algorithms. These
algorithms lead to the mentioned speed-up through their batch-wise execution in
order to drastically reduce the number of shots needed for the real quantum
device compared to standard loop-based optimizers. Their incremental nature
serves the purpose of big data online streaming for DGA botnet detection. These
two steps allow us to apply hybrid quantum machine learning to the field of
cybersecurity analytics on the example of DGA botnet detection and how
quantum-enhanced SIEM and, thereby, quantum cybersecurity analytics is made
possible. We conduct experiments using the library Qiskit with quantum
simulator Aer as well as on three different real quantum devices from MS Azure
Quantum, naming IonQ, Rigetti and Quantinuum. It is the first time that these
tools have been combined.Comment: 33 pages, 6 figures, 6 table
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