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

Naturally Rehearsing Passwords

We introduce quantitative usability and security models to guide the design of password management schemes --- systematic strategies to help users create and remember multiple passwords. In the same way that security proofs in cryptography are based on complexity-theoretic assumptions (e.g., hardness of factoring and discrete logarithm), we quantify usability by introducing usability assumptions. In particular, password management relies on assumptions about human memory, e.g., that a user who follows a particular rehearsal schedule will successfully maintain the corresponding memory. These assumptions are informed by research in cognitive science and validated through empirical studies. Given rehearsal requirements and a user's visitation schedule for each account, we use the total number of extra rehearsals that the user would have to do to remember all of his passwords as a measure of the usability of the password scheme. Our usability model leads us to a key observation: password reuse benefits users not only by reducing the number of passwords that the user has to memorize, but more importantly by increasing the natural rehearsal rate for each password. We also present a security model which accounts for the complexity of password management with multiple accounts and associated threats, including online, offline, and plaintext password leak attacks. Observing that current password management schemes are either insecure or unusable, we present Shared Cues--- a new scheme in which the underlying secret is strategically shared across accounts to ensure that most rehearsal requirements are satisfied naturally while simultaneously providing strong security. The construction uses the Chinese Remainder Theorem to achieve these competing goals

On Enhancing Security of Password-Based Authentication

Password has been the dominant authentication scheme for more than 30 years, and it will not be easily replaced in the foreseeable future. However, password authentication has long been plagued by the dilemma between security and usability, mainly due to human memory limitations. For example, a user often chooses an easy-to-guess (weak) password since it is easier to remember. The ever increasing number of online accounts per user even exacerbates this problem. In this dissertation, we present four research projects that focus on the security of password authentication and its ecosystem. First, we observe that personal information plays a very important role when a user creates a password. Enlightened by this, we conduct a study on how users create their passwords using their personal information based on a leaked password dataset. We create a new metric---Coverage---to quantify the personal information in passwords. Armed with this knowledge, we develop a novel password cracker named Personal-PCFG (Probabilistic Context-Free Grammars) that leverages personal information for targeted password guessing. Experiments show that Personal-PCFG is much more efficient than the original PCFG in cracking passwords. The second project aims to ease the password management hassle for a user. Password managers are introduced so that users need only one password (master password) to access all their other passwords. However, the password manager induces a single point of failure and is potentially vulnerable to data breach. To address these issues, we propose BluePass, a decentralized password manager that features a dual-possession security that involves a master password and a mobile device. In addition, BluePass enables a hand-free user experience by retrieving passwords from the mobile device through Bluetooth communications. In the third project, we investigate an overlooked aspect in the password lifecycle, the password recovery procedure. We study the password recovery protocols in the Alexa top 500 websites, and report interesting findings on the de facto implementation. We observe that the backup email is the primary way for password recovery, and the email becomes a single point of failure. We assess the likelihood of an account recovery attack, analyze the security policy of major email providers, and propose a security enhancement protocol to help securing password recovery emails by two factor authentication. \newline Finally, we focus on a more fundamental level, user identity. Password-based authentication is just a one-time checking to ensure that a user is legitimate. However, a user\u27s identity could be hijacked at any step. For example, an attacker can leverage a zero-day vulnerability to take over the root privilege. Thus, tracking the user behavior is essential to examine the identity legitimacy. We develop a user tracking system based on OS-level logs inside an enterprise network, and apply a variety of techniques to generate a concise and salient user profile for identity examination

Introducing a Machine Learning Password Metric Based on EFKM Clustering Algorithm

we introduce a password strength metric using Enhanced Fuzzy K-Means clustering algorithm (EFKM henceforth). The EFKM is trained on the OWASP list of 10002 weak passwords. After that, the optimized centroids are maximized to develop a password strength metric. The resulting meter was validated by contrasting with three entropy-based metrics using two datasets: the training dataset (OWASP) and a dataset that we collected from github website that contains 5189451 leaked passwords. Our metric is able to recognize all the passwords from the OWASP as weak passwords only. Regarding the leaked passwords, the metric recognizes almost the entire set as weak passwords. We found that the results of the EFKM-based metric and the entropy-based meters are consistent. Hence the EFKM metric demonstrates its validity as an efficient password strength checker

Evaluating the Usability of a Multilingual Passphrase Policy

The literature shows that users struggle to generate secure passwords. This has led to systems administrators implementing password expiry policies that burden and frustrate users. This study explores the security and usability of a multilingual passphrase policy, as multilingualism has the potential to enhance security. A total of 224 participants were invited to participate in an experiment to generate and recall short passwords and multilingual passphrases. The findings of this study show that, although a multilingual passphrase policy made passphrase generation slightly more difficult, its use motivated users to generate unique memorable passphrases. Arguably, repeated use of passphrases promotes memorability and cognitive fluency. Furthermore, the multilingual passphrases in this study proved to be stronger than those reported in the literature

Improving the Eco-system of Passwords

Password-based authentication is perhaps the most widely used method for user authentication. Passwords are both easy to understand and use, and easy to implement. With these advantages, password-based authentication is likely to stay as an important part of security in the foreseeable future. One major weakness of password-based authentication is that many users tend to choose weak passwords that are easy to guess. In this dissertation, we address the challenge and improve the eco-system of passwords in multiple aspects. Firstly, we provide methodologies that help password research. To be more specific, we propose Probability Threshold Graphs, which is superior to Guess Number Graphs when comparing password models and password datasets. We also introduce rich literature of statistical language modeling into password modeling and show that if used correctly, whole-string Markov models outperform Probabilistic Context Free Grammar models. Secondly, we improve password policies and practice used by websites by studying how to best check weak passwords. We model different password strength checking methods as Password Ranking Algorithms (PRAs), and introduce two methods for comparing different PRAs: the β-Residual Strength Graph and the Normalized β-Residual Strength Graph. Finally, we examine the security and usability of commonly suggested password generation strategies. We find that for mnemonic sentence-based strategies, differences in the exact instructions have a tremendous impact on the security level of the resulting passwords. For word-based strategies, security of the resulting passwords mainly depends on the number of words required, and requiring at least 3 words is likely to result in passwords stronger than the general passwords chosen by typical users

PassViz: A Visualisation System for Analysing Leaked Passwords

Passwords remain the most widely used form of user authentication, despite advancements in other methods. However, their limitations, such as susceptibility to attacks, especially weak passwords defined by human users, are well-documented. The existence of weak human-defined passwords has led to repeated password leaks from websites, many of which are of large scale. While such password leaks are unfortunate security incidents, they provide security researchers and practitioners with good opportunities to learn valuable insights from such leaked passwords, in order to identify ways to improve password policies and other security controls on passwords. Researchers have proposed different data visualisation techniques to help analyse leaked passwords. However, many approaches rely solely on frequency analysis, with limited exploration of distance-based graphs. This paper reports PassViz, a novel method that combines the edit distance with the t-SNE (t-distributed stochastic neighbour embedding) dimensionality reduction algorithm for visualising and analysing leaked passwords in a 2-D space. We implemented PassViz as an easy-to-use command-line tool for visualising large-scale password databases, and also as a graphical user interface (GUI) to support interactive visual analytics of small password databases. Using the "000webhost" leaked database as an example, we show how PassViz can be used to visually analyse different aspects of leaked passwords and to facilitate the discovery of previously unknown password patterns. Overall, our approach empowers researchers and practitioners to gain valuable insights and improve password security through effective data visualisation and analysis