5,092 research outputs found
A New Targeted Password Guessing Model
TarGuess-I is a leading targeted password guessing model using users\u27 personally identifiable information(PII) proposed at ACM CCS 2016 by Wang et al. Owing to its superior guessing performance, TarGuess-I has attracted widespread attention in password security. Yet, TarGuess-I fails to capture popular passwords and special strings in passwords correctly. Thus we propose TarGuess-I: an improved password guessing model, which is capable of identifying popular passwords by generating top-300 most popular passwords from similar websites and grasping special strings by extracting continuous characters from user-generated PII. We conduct a series of experiments on 6 real-world leaked datasets and the results show that our improved model outperforms TarGuess-I by 9.07\% on average with 1000 guesses, which proves the effectiveness of our improvements
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NAVI: Novel authentication with visual information
Text-based passwords, despite their well-known drawbacks, remain the dominant user authentication scheme implemented. Graphical password systems, based on visual information such as the recognition of photographs and / or pictures, have emerged as a promising alternative to the aggregate reliance on text passwords. Nevertheless, despite the advantages offered they have not been widely used in practice since many open issues need to be resolved. In this paper we propose a novel graphical password scheme, NAVI, where the credentials of the user are his username and a password formulated by drawing a route on a predefined map. We analyze the strength of the password generated by this scheme and present a prototype implementation in order to illustrate the feasibility of our proposal. Finally, we discuss NAVI’s security features and compare it with existing graphical password schemes as well as text-based passwords in terms of key security features, such aspassword keyspace, dictionary attacks and guessing attacks. The proposed scheme appears to have the same or better performance in the majority of the security features examined
Investigating the Distribution of Password Choices
In this paper we will look at the distribution with which passwords are
chosen. Zipf's Law is commonly observed in lists of chosen words. Using
password lists from four different on-line sources, we will investigate if
Zipf's law is a good candidate for describing the frequency with which
passwords are chosen. We look at a number of standard statistics, used to
measure the security of password distributions, and see if modelling the data
using Zipf's Law produces good estimates of these statistics. We then look at
the the similarity of the password distributions from each of our sources,
using guessing as a metric. This shows that these distributions provide
effective tools for cracking passwords. Finally, we will show how to shape the
distribution of passwords in use, by occasionally asking users to choose a
different password
ZETA - Zero-Trust Authentication: Relying on Innate Human Ability, not Technology
Reliable authentication requires the devices and
channels involved in the process to be trustworthy; otherwise
authentication secrets can easily be compromised. Given the
unceasing efforts of attackers worldwide such trustworthiness
is increasingly not a given. A variety of technical solutions,
such as utilising multiple devices/channels and verification
protocols, has the potential to mitigate the threat of untrusted
communications to a certain extent. Yet such technical solutions
make two assumptions: (1) users have access to multiple
devices and (2) attackers will not resort to hacking the human,
using social engineering techniques. In this paper, we propose
and explore the potential of using human-based computation
instead of solely technical solutions to mitigate the threat of
untrusted devices and channels. ZeTA (Zero Trust Authentication
on untrusted channels) has the potential to allow people to
authenticate despite compromised channels or communications
and easily observed usage. Our contributions are threefold:
(1) We propose the ZeTA protocol with a formal definition
and security analysis that utilises semantics and human-based
computation to ameliorate the problem of untrusted devices
and channels. (2) We outline a security analysis to assess
the envisaged performance of the proposed authentication
protocol. (3) We report on a usability study that explores the
viability of relying on human computation in this context
Why Botnets Work: Distributed Brute-Force Attacks Need No Synchronization
In September 2017, McAffee Labs quarterly report estimated that brute force
attacks represent 20\% of total network attacks, making them the most prevalent
type of attack ex-aequo with browser based vulnerabilities. These attacks have
sometimes catastrophic consequences, and understanding their fundamental limits
may play an important role in the risk assessment of password-secured systems,
and in the design of better security protocols. While some solutions exist to
prevent online brute-force attacks that arise from one single IP address,
attacks performed by botnets are more challenging. In this paper, we analyze
these distributed attacks by using a simplified model. Our aim is to understand
the impact of distribution and asynchronization on the overall computational
effort necessary to breach a system. Our result is based on Guesswork, a
measure of the number of queries (guesses) required of an adversary before a
correct sequence, such as a password, is found in an optimal attack. Guesswork
is a direct surrogate for time and computational effort of guessing a sequence
from a set of sequences with associated likelihoods. We model the lack of
synchronization by a worst-case optimization in which the queries made by
multiple adversarial agents are received in the worst possible order for the
adversary, resulting in a min-max formulation. We show that, even without
synchronization, and for sequences of growing length, the asymptotic optimal
performance is achievable by using randomized guesses drawn from an appropriate
distribution. Therefore, randomization is key for distributed asynchronous
attacks. In other words, asynchronous guessers can asymptotically perform
brute-force attacks as efficiently as synchronized guessers.Comment: Accepted to IEEE Transactions on Information Forensics and Securit
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