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The underlying issues relating to the usability and security of multiple passwords are largely unexplored. However, we know that people generally have difficulty remembering multiple passwords. This reduces security since users reuse the same password for different systems or reveal other passwords as they try to log in. We report on a laboratory study comparing recall of multiple text passwords with recall of multiple click-based graphical passwords. In a one-hour session (short-term), we found that participants in the graphical password condition coped significantly better than those in the text password condition. In particular, they made fewer errors when recalling their passwords, did not resort to creating passwords directly related to account names, and did not use similar passwords across multiple accounts. After two weeks, participants in the two conditions had recall success rates that were not statistically different from each other, but those with text passwords made more recall errors than participants with graphical passwords. In our study, click-based graphical passwords were significantly less susceptible to multiple password interference in the short-term, while having comparable usability to text passwords in most other respects.

Abstract A survey evaluating the generation and use of passwords revealed that students have 8.18 password uses. With 4.45 different passwords to cover these functions, the average password has 1.84 applications. Two thirds of passwords are designed around one's personal characteristics, with most of the remainder relating to relatives, friends or lovers. Proper names and birthdays are the primary information used in constructing passwords, accounting for about half of all password constructions. Almost all respondents reuse passwords, and about two thirds of password uses are duplications. Passwords have been forgotten by a third of respondents, and over half keep a written record of them. We found empirical confirmation of some ‘bad password practices’ discussed in the literature, and provide suggestions for password construction and use. Copyright © 2004 John Wiley & Sons, Ltd.

Text-based passwords remain the dominant authentication method in computer systems, despite significant advancement in attackers' capabilities to perform password cracking. In response to this threat, password composition policies have grown increasingly complex. However, there is insufficient research defining metrics to characterize password strength and using them to evaluate password-composition policies. In this paper, we analyze 12,000 passwords collected under seven composition policies via an online study. We develop an efficient distributed method for calculating how effectively several heuristic password-guessing algorithms guess passwords. Leveraging this method, we investigate (a) the resistance of passwords created under different conditions to guessing, (b) the performance of guessing algorithms under different training sets, (c) the relationship between passwords explicitly created under a given composition policy and other passwords that happen to meet the same requirements, and (d) the relationship between guess ability, as measured with password-cracking algorithms, and entropy estimates. Our findings advance understanding of both password-composition policies and metrics for quantifying password security.

Given the widespread use of password authentication in online correspondence, subscription services, and shopping, there is growing concern about identity theft. When people reuse their passwords across multiple accounts, they increase their vulnerability; compromising one password can help an attacker take over several accounts. Our study of 49 undergraduates quantifies how many passwords they had and how often they reused these passwords. The majority of users had three or fewer passwords and passwords were reused twice. Furthermore, over time, password reuse rates increased because people accumulated more accounts but did not create more passwords. Users justified their habits. While they wanted to protect financial data and personal communication, reusing passwords made passwords easier to manage. Users visualized threats from human attackers, particularly viewing those close to them as the most motivated and able attackers; however, participants did not separate the human attackers from their potentially automated tools. They sometimes failed to realize that personalized passwords such as phone numbers can be cracked given a large enough dictionary and enough tries. We discuss how current systems support poor password practices. We also present potential changes in website authentication systems and password managers.

It is a well known fact that user-chosen passwords are somewhat predictable: by using tools such as dictionaries or probabilistic models, attackers and password recovery tools can drastically reduce the number of attempts needed to guess a password. Quite surprisingly, however, existing literature does not provide a satisfying answer to the following question: given a number of guesses, what is the probability that a state-of-the-art attacker will be able to break a password? To answer the former question, we compare and evaluate the effectiveness of currently known attacks using various datasets of known passwords. We find that a "diminishing returns" principle applies: in the absence of an enforced password strength policy, weak passwords are common; on the other hand, as the attack goes on, the probability that a guess will succeed decreases by orders of magnitude. Even extremely powerful attackers won't be able to guess a substantial percentage of the passwords. The result of this work will help in evaluating the security of authentication means based on user- chosen passwords, and our methodology for estimating password strength can be used as a basis for creating more effective proactive password checkers for users and security auditing tools for administrators.

Choosing the most effective word-mangling rules to use when performing a dictionary-based password cracking attack can be a difficult task. In this paper we discuss a new method that generates password structures in highest probability order. We first automatically create a probabilistic context-free grammar based upon a training set of previously disclosed passwords. This grammar then allows us to generate word-mangling rules, and from them, password guesses to be used in password cracking. We will also show that this approach seems to provide a more effective way to crack passwords as compared to traditional methods by testing our tools and techniques on real password sets. In one series of experiments, training on a set of disclosed passwords, our approach was able to crack 28% to 129% more passwords than John the Ripper, a publicly available standard password cracking program.

Text-based passwords are the most common mechanism for authenticating humans to computer systems. To prevent users from picking passwords that are too easy for an adversary to guess, system administrators adopt password-composition policies (e.g., requiring passwords to contain symbols and numbers). Unfortunately, little is known about the relationship between password-composition policies and the strength of the resulting passwords, or about the behavior of users (e.g., writing down passwords) in response to different policies. We present a large-scale study that investigates password strength, user behavior, and user sentiment across four password-composition policies. We characterize the predictability of passwords by calculating their entropy, and find that a number of commonly held beliefs about password composition and strength are inaccurate. We correlate our results with user behavior and sentiment to produce several recommendations for password-composition policies that result in strong passwords without unduly burdening users.

We report the results of a large scale study of password use andpassword re-use habits. The study involved half a million users over athree month period. A client component on users' machines recorded a variety of password strength, usage and frequency metrics. This allows us to measure or estimate such quantities as the average number of passwords and average number of accounts each user has, how many passwords she types per day, how often passwords are shared among sites, and how often they are forgotten. We get extremely detailed data on password strength, the types and lengths of passwords chosen, and how they vary by site. The data is the first large scale study of its kind, and yields numerous other insights into the role the passwords play in users' online experience.

Human-memorable passwords are a mainstay of computer security. To decrease vulnerability of passwords to brute-force dictionary attacks, many organizations enforce complicated password-creation rules and require that passwords include numerals and special characters. We demonstrate that as long as passwords remain human-memorable, they are vulnerable to "smart-dictionary" attacks even when the space of potential passwords is large.Our first insight is that the distribution of letters in easy-to-remember passwords is likely to be similar to the distribution of letters in the users' native language. Using standard Markov modeling techniques from natural language processing, this can be used to dramatically reduce the size of the password space to be searched. Our second contribution is an algorithm for efficient enumeration of the remaining password space. This allows application of time-space tradeoff techniques, limiting memory accesses to a relatively small table of "partial dictionary" sizes and enabling a very fast dictionary attack.We evaluated our method on a database of real-world user password hashes. Our algorithm successfully recovered 67.6% of the passwords using a 2 x 109 search space. This is a much higher percentage than Oechslin's "rainbow" attack, which is the fastest currently known technique for searching large keyspaces. These results call into question viability of human-memorable character-sequence passwords as an authentication mechanism.

A probabilistic password model assigns a probability value to each string. Such models are useful for research into understanding what makes users choose more (or less) secure passwords, and for constructing password strength meters and password cracking utilities. Guess number graphs generated from password models are a widely used method in password research. In this paper, we show that probability-threshold graphs have important advantages over guess-number graphs. They are much faster to compute, and at the same time provide information beyond what is feasible in guess-number graphs. We also observe that research in password modeling can benefit from the extensive literature in statistical language modeling. We conduct a systematic evaluation of a large number of probabilistic password models, including Markov models using different normalization and smoothing methods, and found that, among other things, Markov models, when done correctly, perform significantly better than the Probabilistic Context-Free Grammar model proposed in Weir et al., which has been used as the state-of-the-art password model in recent research.

The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, we conduct a comprehensive survey of the existing graphical password techniques. We classify these techniques into two categories: recognition-based and recall-based approaches. We discuss the strengths and limitations of each method and point out the future research directions in this area. We also try to answer two important questions: "Are graphical passwords as secure as text-based passwords?"; "What are the major design and implementation issues for graphical passwords?" This survey will be useful for information security researchers and practitioners who are interested in finding an alternative to text-based authentication methods

In this paper we attempt to determine the effectiveness of using entropy, as defined in NIST SP800-63, as a measurement of the security provided by various password creation policies. This is accomplished by modeling the success rate of current password cracking techniques against real user passwords. These data sets were collected from several different websites, the largest one containing over 32 million passwords. This focus on actual attack methodologies and real user passwords quite possibly makes this one of the largest studies on password security to date. In addition we examine what these results mean for standard password creation policies, such as minimum password length, and character set requirements.

Text-based passwords are still the most commonly used authentication mechanism in information systems. We took advantage of a unique opportunity presented by a significant change in the Carnegie Mellon University (CMU) computing services password policy that required users to change their passwords. Through our survey of 470 CMU computer users, we collected data about behaviors and practices related to the use and creation of passwords. We also captured users' opinions about the new, stronger policy requirements. Our analysis shows that, although most of the users were annoyed by the need to create a complex password, they believe that they are now more secure. Furthermore, we perform an entropy analysis and discuss how our findings relate to NIST recommendations for creating a password policy. We also examine how users answer specific questions related to their passwords. Our results can be helpful in designing better password policies that consider not only technical aspects of specific policy rules, but also users' behavior in response to those rules.

In this paper we propose and evaluate new graphical password schemes that exploit features of graphical input displays to achieve better security than text-based passwords. Graphical input devices enable the user to decouple the position of inputs from the temporal order in which those inputs occur, and we show that this decoupling can be used to generate password schemes with substantially larger #memorable# password spaces. In order to evaluate the security of one of our schemes, we devise a novel way to capture a subset of the #memorable" passwords that, we believe, is itself a contribution. In this work we are primarily motivated by devices such as personal digital assistants #PDAs# that o#er graphical input capabilities via a stylus, and we describe our prototype implementation of one of our password schemes on suchaPDA, namely the Palm Pilot TM . 1 Introduction For the vast majority of computer systems, passwords are the method of choice for authenticating users. It ...

We describe a browser extension, PwdHash, that transparently produces a different password for each site, improving web password security and defending against password phishing and other attacks. Since the browser extension applies a cryptographic hash function to a combination of the plaintext password entered by the user, data associated with the web site, and (optionally) a private salt stored on the client machine, theft of the password received at one site will not yield a password that is useful at another site. While the scheme requires no changes on the server side, implementing this password method securely and transparently in a web browser extension turns out to be quite difficult. We describe the challenges we faced in implementing PwdHash and some techniques that may be useful to anyone facing similar security issues in a browser environment.

Graphical passwords are an alternative to alphanumeric passwords in which users click on images to authenticate themselves rather than type alphanumeric strings. We have developed one such system, called PassPoints, and evaluated it with human users. The results of the evaluation were promising with respect to rmemorability of the graphical password. In this study we expand our human factors testing by studying two issues: the effect of tolerance, or margin of error, in clicking on the password points and the effect of the image used in the password system. In our tolerance study, results show that accurate memory for the password is strongly reduced when using a small tolerance (10 x 10 pixels) around the user's password points. This may occur because users fail to encode the password points in memory in the precise manner that is necessary to remember the password over a lapse of time. In our image study we compared user performance on four everyday images. The results indicate that there were few significant differences in performance of the images. This preliminary result suggests that many images may support memorability in graphical password systems.

This paper presents a new password authentication and key-exchange protocol suitable for authenticating users and exchanging keys over an untrusted network. The new protocol resists dictionary attacks mounted by either passive or active network intruders, allowing, in principle, even weak passphrases to be used safely. It also offers perfect forward secrecy, which protects past sessions and passwords against future compromises. Finally, user passwords are stored in a form that is not plaintext-equivalent to the password itself, so an attacker who captures the password database cannot use it directly to compromise security and gain immediate access to the host. This new protocol combines techniques of zero-knowledge proofs with asymmetric key exchange protocols and offers significantly improved performance over comparably strong extended methods that resist stolen-verifier attacks such as Augmented EKE or B-SPEKE. 1 Introduction Password authentication protocols come in many flavors, b...

When users input their passwords in a public place, they may be at risk of attackers stealing their password. An attacker can capture a password by direct observation or by recording the individual's authentication session. This is referred to as shoulder-surfing and is a known risk, of special concern when authenticating in public places. Until recently, the only defense against shoulder-surfing has been vigilance on the part of the user. This paper reports on the design and evaluation of a game-like graphical method of authentication that is resistant to shoulder-surfing. The Convex Hull Click (CHC) scheme allows a user to prove knowledge of the graphical password safely in an insecure location because users never have to click directly on their password images. Usability testing of the CHC scheme showed that novice users were able to enter their graphical password accurately and to remember it over time. However, the protection against shoulder-surfing comes at the price of longer time to carry out the authentication.

A remote password authentication scheme based on the Chinese remainder theorem is proposed. The scheme can verify the remote password without verification tables. In the initial phase, the password generation centre generates and assigns a password corresponding to each user. The ideas of smart cards and the identity-based signature scheme introduced by Shamir are employed in this phase. Each user possesses a smart card for later login and authentication. In the login phase, the user submits the identity and password associated with the smart card. In the authentication phase, the system verifies the remotely submitted password to check if the login request is accepted or rejected. A signature scheme and communication timestamps are provided in the authentication phase against the potential attacks of replaying a previously intercepted login request.