166 research outputs found
Password Cracking and Countermeasures in Computer Security: A Survey
With the rapid development of internet technologies, social networks, and
other related areas, user authentication becomes more and more important to
protect the data of the users. Password authentication is one of the widely
used methods to achieve authentication for legal users and defense against
intruders. There have been many password cracking methods developed during the
past years, and people have been designing the countermeasures against password
cracking all the time. However, we find that the survey work on the password
cracking research has not been done very much. This paper is mainly to give a
brief review of the password cracking methods, import technologies of password
cracking, and the countermeasures against password cracking that are usually
designed at two stages including the password design stage (e.g. user
education, dynamic password, use of tokens, computer generations) and after the
design (e.g. reactive password checking, proactive password checking, password
encryption, access control). The main objective of this work is offering the
abecedarian IT security professionals and the common audiences with some
knowledge about the computer security and password cracking, and promoting the
development of this area.Comment: add copyright to the tables to the original authors, add
acknowledgement to helpe
Cracking-Resistant Password Vaults using Natural Language Encoders
Password vaults are increasingly popular applications that store multiple passwords encrypted under a single master password that the user memorizes. A password vault can greatly reduce the burden on a user of remembering passwords, but introduces a single point of failure. An attacker that obtains a user’s encrypted vault can mount offline brute-force attacks and, if successful, compromise all of the passwords in the vault. In this paper, we investigate the construction of encrypted vaults that resist such offline cracking attacks and force attackers instead to mount online attacks.
Our contributions are as follows. We present an attack and supporting analysis showing that a previous design for cracking-resistant vaults—the only one of which we are aware—actually degrades security relative to conventional password-based approaches. We then introduce a new type of secure encoding scheme that we call a natural language encoder (NLE). An NLE permits the construction of vaults which, when
decrypted with the wrong master password, produce plausible-looking decoy passwords. We show how to build NLEs using existing tools from natural language processing, such as n-gram models and probabilistic context-free grammars, and evaluate their ability to generate plausible decoys. Finally, we present, implement, and evaluate a full, NLE-based cracking-resistant vault system called NoCrack
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
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
Password Breach Protection Using Honeyword
With the advancement in the field of information technology, many users have share their files on cloud are bound to have many security related issues. One of them is password file. Password files have got more security problems which affect millions of customers worldwide as well as many organizations. In Existing system, password is stored in the encrypted form, if password stolen, then by cracking of password technique and decryption method which very easy to get most of plaintext and encrypted passwords. In propose work, it generates honeyword passwords i.e. untrue passwords by using flawlessly honeyword generation way, and also it tries to invite prohibited or unauthorized users. Hence we notice the illegal users. Here also protects original info from illegal users using other file format
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