Explore security upgrades that complement the existing system.
Electronic copy available at: http://ssrn.com/abstract=2292761
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Jeffrey L. Jenkins, Mark Grimes, Jeff Proudfoot, and Paul Benjamin Lowry (2013). “Improving password cybersecurity through inexpensive and minimally invasive means: Detecting and deterring password reuse through keystroke-dynamics monitoring and just-in-time warnings,” Information Technology for Development (accepted 09-June-2013).
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Electronic copy available at: http://ssrn.com/abstract=2292761
Improving password cybersecurity through inexpensive and minimally
invasive means: Detecting and deterring password reuse through
keystroke-dynamics monitoring and just-in-time fear appeals
Password reuse—using the same password for multiple accounts—is a prevalent
phenomenon that can make even the most secure systems vulnerable. When
passwords are reused across multiple systems, hackers may compromise accounts
by stealing passwords from low-security sites to access sites with higher security.
Password reuse can be particularly threatening to users in developing countries in
which cybersecurity training is limited, law enforcement of cybersecurity is non-
existent, or in which programs to secure cyberspace are limited. This article
proposes a two-pronged solution for reducing password reuse through detection
and mitigation. First, based on the theories of routine, cognitive load, and motor
movement, we hypothesize that password reuse can be detected by monitoring
characteristics of users’ typing behavior (i.e., keystroke dynamics). Second, based
on protection motivation theory, we hypothesize that providing just-in-time fear
appeals when a violation is detected will decrease password reuse. We tested our
hypotheses in an experiment and found that users’ keystroke dynamics are
diagnostic of password reuse. By analyzing changes in typing patterns, we were
able to detect password reuse with 81.71% accuracy. We also found that just-in-
time fear appeals decrease password reuse; 88.41% of users who received a fear
appeal subsequently created unique passwords, whereas only 4.45% of users who
did not receive a fear appeal created unique passwords. Our results suggest that
future research should continue to examine keystroke dynamics as an indicator of
cybersecurity behaviours, and use just-in-time fear appeals as a method for
reducing non-secure behavior. The findings of our research provide a practical
and cost-effective solution to bolster cybersecurity through discouraging
Keywords: password reuse; keystroke dynamics; protection motivation theory;
just-in-time fear appeals; support vector machine; cybersecurity; developing
Global information and communication technology is rapidly growing (Maurer,
2011); however, developing countries frequently lack adequate cybersecurity controls
and opportunities to educate the public about cybersecurity1 (Chen, et al., 2006).
Attackers (e.g., hackers, fraudsters) therefore target users from developing nations
because they perceive that these regions do not have programs to secure cyberspace or
to enforce cybersecurity laws (Tagert, 2010). Through gaining unauthorized access to
user accounts, these attackers commit cybercrimes (e.g., identity theft; theft of money;
phishing; unauthorized access to organizational or governmental resources) or even
cyber warfare against other nations through espionage, sabotage, denial of service
attacks, and malicious code hosting (Maurer, 2011; Tagert, 2010).
One common tactic leveraged to attack users is through exploiting password
reuse by taking advantage of a user who has an identical password across multiple
accounts (Ives, Walsh, & Schneider, 2004; Notoatmodjo & Thomborson, 2009). When
password reuse occurs, hackers have the ability to steal credentials from one site (e.g., a
low-security or fraudulent website hosted in a developing country) and use these
credentials to breach other sites, even those that have considerable cybersecurity
controls in place (Notoatmodjo & Thomborson, 2009). This phenomenon is often
referred to as the ‘Domino Effect’—after the failure of the weakest system, other
systems will follow, yielding new password information from which still more systems
1 Cybersecurity is used to refer to the body of practices, technologies, and processes designed to
protect cyber-infrastructure (e.g., data, networks, computers, software) and user assets from
attack, damage, and unauthorized access and use (Brechbuhl, et al., 2010)
will fail (Ives, et al., 2004).
Despite the dangers of password reuse, it is very prevalent. A global study of
over 500,000 users found that people, on average, access 25 password-protected sites
using only 6.5 unique passwords, resulting in passwords being shared across 3.9 sites on
average (Florencio & Herley, 2007). Users suffer from ‘password-overload’ with over
half of individuals surveyed admitting to having reused a password for highly-important
accounts (Notoatmodjo & Thomborson, 2009). Several potential solutions have been
proposed to deter password reuse, such as password managers (Gaw & Felten, 2006),
alternative authentication methods (Ives, et al., 2004), and composition rules (Campbell,
Ma, & Kleeman, 2011). Although some of these techniques have shown utility—
particularly in an organizational setting—their widespread adoption is low because of
high costs, mass-deployability issues, or technical limitations (Bonneau, et al., 2012).
These problems are amplified in developing nations in which cyber-infrastructure is
poor, IT knowledge lags behind (Carte, Dharmasiri, & Perera, 2011; Pick & Azari,
2008), public/shared computer use is high (Florencio & Herley, 2006), and financial
resources are scarce relative to other nations (Roztocki & Weistroffer, 2011) (see Table
1 in the literature review section for a more detailed discussion).
We propose a cost-effective methodology for mitigating password reuse by
detecting when it occurs, and then displaying just-in-time fear appeals. By
implementing these warnings on an “as needed” basis, we intend to target users who are
likely reusing passwords while not inconveniencing those who are likely creating
unique passwords. This can provide a cost-effective remedy that is feasible to
implement in the developing world. In this article, we describe how password reuse can
be identified by monitoring a user’s keystroke behavior (i.e., keystroke dynamics). We
then explain why just-in-time fear appeals provided on an “as needed” basis will
discourage password reuse. In summary, we help address the need to decrease password
reuse by answering the following questions:
RQ1. Can keystroke dynamics be used to predict if individuals are reusing
RQ2. Does providing just-in-time fear appeals help reduce password reuse?
Recognizing the dangers of password reuse, researchers have been challenged to
better understand and create solutions to mitigate password reuse (Ives, et al., 2004).
Table 1 reviews selected password reuse deterrence strategies identified in the literature,
and their applicability to the developing world. One cause of password reuse is that
users are often unaware of what constitutes good password practices (Furnell, 2007).
Although previous studies have shown that training may increase awareness and thereby
reduce password reuse (Charoen, Murali, & Lorne, 2008; Ives, et al., 2004), training
alone is rarely sufficient. For example, in a developing-world context, users in Nigeria
who were given brief training on improving passwords by using mnemonics simply
used easily-memorable, easily-hacked mnemonics rather than unique strong passwords
(Oghenerukevbe, 2010). Other research suggests that even if users know what
constitutes good or bad password practices, they have little motivation to comply
because they perceive little threat and do not want to be inconvenienced; and thus, users
gravitate toward the path of least resistance (Tam, Glassman, & Vandenwauver, 2010;
Zhang & McDowell, 2009).
One suggested way to overcome these limitations is through fear appeals
(Herath & Rao, 2009; Johnston & Warkentin, 2010). Generally, a fear appeal is a
persuasive messaged intended to better help someone be aware of a threat and to
persuade them to engage in a protective action. Fear appeals can be implemented to help
increase users’ perceptions of the costs and dangers of reusing passwords (Zhang &
McDowell, 2009); the more probable people perceive that their accounts can be
compromised, the less likely they are to reuse passwords (Bryant & Campbell, 2006).
Table 1. Examples of Password Reuse Deterrence Strategies Password Reuse Solution Effectiveness in Developing Nations Client-side password management systems (Gaw & Felten, 2006)
Computer use often occurs on public computers and in computer cafés in developing nations where the use of password managers may not be feasible (Florencio & Herley, 2006)
Composition rules / password requirements (Campbell, Kleeman, & Ma, 2007; Campbell, et al., 2011; Keith, Shao, & Steinbart, 2009)
While composition policies reduce the use of dictionary words and similar words, they do not normally reduce password reuse (Campbell, et al., 2011)
Alternative forms of authentication (Ives, et al., 2004), for example: neural networks (Shouhong & Hai, 2008) graphical passwords (Biddle, Chiasson, &
Van Orschot, 2012) smart cards (Hwang, Chong, & Chen,
2010) one-time passwords using specialized
servers (Huang, Ma, & Chen, 2011)
Although useful in many organizational settings, these alternative forms of authentication have low public penetration because “none… retains the full set of benefits that legacy passwords already provide” (Bonneau, et al., 2012, p. 1).
Training (Charoen, et al., 2008; Ives, et al., 2004)
Normally not sufficient alone to deter password reuse in developing nations (e.g., Oghenerukevbe, 2010); there are limited opportunities to mass educate the public in developing nations (Cone, et al., 2007).
Fear appeals are explained by a number of theories, protection motivation theory
(PMT) being among the most developed (Rogers, 1975; Rogers, 1983). Johnston and
Warkentin (2010) conducted a PMT-based study that concluded fear appeals can be
used to positively influence users’ intentions to comply with individual acts of
cybersecurity. In their study, perceptions of self-efficacy, response efficacy, and threat
severity were found to inform the degree to which fear appeals impacted behavioral
intentions. An organizational survey based on PMT used perceived severity, rewards,
response efficacy, self-efficacy, and response costs to predict intentions to comply with
IS security policies (Vance, Siponen, & Pahnila, 2012). Similar results were seen in
(Posey, et al., 2011). Another multi-organizational survey determined that perceived
severity, response efficacy, self-efficacy, and response cost influence users’ attitudes
toward cybersecurity policies (Herath & Rao, 2009). A survey of college students
showed that PMT is effective in increasing users’ willingness to use strong passwords
(Zhang & McDowell, 2009). A larger survey of non-students also demonstrated this
tendency (Milne, Labrecque, & Cromer, 2009). Finally, fear appeals have been shown
to influence specific cybersecurity behaviors such as updating and protecting passwords
(Workman, Bommer, & Straub, 2008).
Our paper extends these findings to examine whether the use of just-in-time fear
appeals, in response to the detection of password reuse, will discourage password reuse.
We explore using keystroke dynamics as a minimally invasive and low-cost method for
identifying password reuse and triggering a just-in-time fear appeal—properties that
make it a compelling solution for developing countries. Keystroke dynamics are the
behavioral characteristics of how a user types. Gaines et al. (1980) collected precise
measurements of keystroke timings and identified two attributes that are useful in
developing a keystroke signature for a user: (1) dwell time (how long a key is held
down), and (2) transition time (the latency between key presses). Based on this
research, many studies have investigated using keystroke dynamics as a method to
supplement the more traditional identification and authentication practice of using
usernames and passwords (Joyce & Gupta, 1990; Leggett & Williams, 1988; Park, Park,
& Cho, 2010; Tappert, Villani, & Cha, 2009; Teh, et al., 2010).
Aside from an identification and authentication context, research in keystroke
dynamics has demonstrated that typing characteristics may be linked to changes in
cognitive load (Vizer, Zhou, & Sears, 2009). Creating unique, strong passwords is a
complex, mentally-taxing task that increases cognitive load (Adams & Sasse, 1999)
resulting in distinct cognitive processing compared to reusing routine passwords. We
thus contend that monitoring changes in keystroke dynamics has the potential for
detecting password reuse. To date, however, researchers have yet to examine whether
keystroke dynamics may be diagnostic of changes in cognitive processing and cognitive
load associated with password reuse, and whether providing a just-in-time fear appeal
will discourage password reuse when detected.
Theory and Hypotheses
We first explain why users should exhibit different keystroke dynamics when
creating a unique password as compared to typing routine information such as their
name, email address, username, or a commonly used password. To do so, we integrate
theories of routine, cognitive load, and motor movement. We then use PMT and theory
on salience to explain why just-in-time fear appeals will decrease password reuse when
violations are detected.
For typists, the cognitive nature of keystroke dynamics differs when typing
routine words verses non-routine words (e.g., unique passwords). Routine words refer to
strings that an individual has typed many times previously, such as their name, email
address, and passwords that they frequently use. Theories explaining routine action
(e.g., Miller, Galanter, & Pribram, 1986) posit that routine tasks are cognitively
hierarchically structured indicating that higher-level goals initiate automatic lower-level
tasks to fluently and rapidly coordinate behavior (Shaffer, 1976). For example, when
typists type routine words, they rarely think about the letters they are typing; instead,
they think of the word, or collection of words, which activates the keystroke-execution
processes for automatically typing each letter in the word (Crump & Logan, 2010).
Logan and Crump explain this phenomenon as the inner–outer loop theory of
typing (Crump & Logan, 2010; Logan & Crump, 2009). This theory divides the
cognitive process of typing into two separate hierarchical processes or ‘loops’: the outer
loop and the inner loop. The outer loop transforms text or thoughts into a series of
words and passes these words to the inner loop. The inner loop is an automatic and
subconscious process that transforms each word into a series of coordinated hand and
finger movements to make up the necessary keystrokes. The inner loop causes parallel
activation of constituent keystrokes for each letter in the word and provides a serial
control process (John, 1996; Wu & Liu, 2008). According to this theory, the outer loop
is unaware of the processes of the inner-loop. Because of this, typists typically have to
think only of the word or sentence rather than thinking of the individual characters or
the layout of the keyboard. Hence, typists have little explicit knowledge of what their
fingers are doing (Logan & Crump, 2009).
This hierarchical nature of typing has received considerable support in literature.
For example, research has shown that the copy-typing rate is dependent on the word-
like structure of the text. Words are typed much faster than random strings of
characters, suggesting that words themselves play an important role in the cognitive
processes that govern typing (Crump & Logan, 2010; Larochelle, 1983; Shaffer &
Hardwick, 1968). Typing speeds are also influenced by word-structure, such as syllable
boundaries, indicating that word-level development is used during typing (Weingarten,
Nottbusch, & Will, 2004; Will, Nottbusch, & Weingarten, 2006). Research has also
shown that the human mind processes the letters in a word or sentence congruently, and
fingers move in parallel to type the words, as opposed to processing each letter
individually (Gentner, Grudin, & Conway, 1980). Finally, when typists are asked to
recall the structure of a keyboard, type the letters for the left or right hand only, or pay
attention to the keys that are being typed, typing performance decreases drastically.
These results suggest that the word or sentence units activate subconscious lower-level
processes that trigger the individual key strokes (Logan, 2003).
When people type non-routine strings of characters, such as a strong unique
password, typing is governed by different cognitive processes than when they are typing
words or strings using the routine processes described previously. Creating a strong
unique password requires substantial cognitive attention (Adams & Sasse, 1999). Strong
passwords can be defined as non-routine words that include upper and lower-case
letters, special characters, considerable length, and digits constructed in a manner such
that the user can remember the password. The additional constraint of uniqueness
requires the individual to create a password that has not been used before, further
increasing cognitive load. During this cognitively demanding password composition
process, typing flow is disturbed. Rather than relying on the outer-inner loop process to
translate words into fluent and efficient keystroke behavior, the requirement to include
special characters, digits, and upper or lower-case letters causes people to become
conscious of what is being typed at the character level. Such behavior has been shown
to significantly alter keystroke dynamics, typically by making it more sporadic and
deliberate (Crump & Logan, 2010; Larochelle, 1983; Shaffer & Hardwick, 1968).
In summary, we predict that keystroke dynamics will differ for individuals
typing in routine information—such as a name, email address, or previously used
passwords—versus individuals typing in non-routine information, as with a unique
password. When users type in routine information, they will produce fluent, efficient,
and consistent typing behavior that is governed by the outer-loop, inner-loop cognitive
processing of words. However, when users create strong and unique passwords, their
keystroke dynamics will be influenced by cognitive load and character-level cognitive
processing. Accordingly, we propose the following hypothesis:
H1. Creating strong unique passwords results in a measurable difference in
keystroke dynamics as compared with typing known information
When password reuse is detected through keystroke dynamics, the system can
utilize just-in-time fear appeals to encourage users to create a unique password. To
explain why fear appeals will decrease password reuse, we leverage PMT. PMT was
originally constructed to help clarify how warnings, known as fear appeals, influence
behavior (Rogers, 1975; Rogers, 1983). Johnston and Warkentin (2010) define a fear
appeal as “…a persuasive message with the intent to motivate individuals to comply
with a recommended course of action through the arousal of fear associated with a
threat” (pp. 550-551). PMT explains that two appraisal processes take place when
individuals are given a warning—a threat appraisal process and a coping appraisal
process. The outcome of these two appraisal processes will determine whether the
warning will be successful in changing behavior (Rogers, 1983; Vance, et al., 2012).
The threat appraisal process consists of assessing threat severity, threat
vulnerability, and the benefit of behaving in a maladaptive manner (i.e., maladaptive
rewards) (Rogers, 1983). In the context of password reuse, severity refers to the degree
of hardship that can result from reusing a password. Passwords provide protection to
many sensitive sites, including banks, email, social networking sites, and so on.
Perceptions of severity include beliefs about the negative consequences that may result
from being hacked due to password reuse on these sites, including identity theft,
financial loss, personal data exposure, and loss of confidential information. The more
severe one perceives the consequences of password reuse, the less likely it is that one
will reuse passwords.
Vulnerability refers to the probability that one believes he/she will experience
harm from reusing a password. Typically, people are in denial about passwords: they
believe that only others with important information will be hacked, and thus they reuse
passwords (Zhang & McDowell, 2009). However, as one’s perception of vulnerability
increases, password reuse should likewise decrease. Finally, the maladaptive benefit
refers to the positive aspects of reusing passwords (e.g., easier to create and remember).
If one perceives the benefit of reusing a password to be lower than the costs (e.g., the
severity of and vulnerability to attack), they will likely not reuse passwords.
The coping-appraisal process consists of assessing response efficacy, self-
efficacy, and response costs (Rogers, 1975). In a password reuse context, response
efficacy is the perceived effectiveness of creating strong unique passwords to avoid
being hacked or becoming a victim of a cybersecurity breach. People who believe that
creating unique passwords reduces susceptibility are likely to avoid password reuse.
Again, in this context, self-efficacy refers to one’s personal belief in his/her
ability to stop reusing passwords. Humans have cognitive limitations that deter them
from making optimal security decisions. Working memory is limited, and therefore
people must rely on strategies to remember unique passwords. The more a person
believes that he or she has the ability and resources to not reuse a password, the more
that individual will avoid password reuse.
Finally, response costs refer to the costs of creating unique passwords. Response
costs include the time and effort spent creating unique passwords, the cost of forgetting
one’s password, the inconvenience of being locked out of a system, and so on. The
frustration and inconvenience of forgetting a password is amplified by the large number
of online accounts and passwords people have to remember. The higher one perceives
the costs of creating unique passwords to be, the more likely one will reuse passwords
(Zhang & McDowell, 2009).
Fear appeals may inspire individuals to create strong and unique passwords
through heightening perceptions of severity and vulnerability (the threat-appraisal
process) as well as increasing perceptions of response efficacy and self-efficacy (the
coping-appraisal process). Rogers (1975) states,
“…a basic postulate is that protection motivation arises from the cognitive
appraisal of a depicted event as noxious and likely to occur, along with the belief
that a recommended coping response can effectively prevent the occurrence of
the aversive event. If an event is not appraised as severe, as likely to occur, or if
nothing can be done about the event, then no protection motivation would be
aroused” (p. 99).
Just-in-time fear appeals should particularly have potential to heighten
perceptions of severity, vulnerability, response efficacy, and self-efficacy because of the
immediacy of the warning to the users’ behavior of creating a unique password.
Immediacy has been shown to increase the salience of beliefs (Crano, 1995). An
individual’s working memory is limited, and information must compete for attention,
therefore, only the most salient information will gain attention and influence behavior
(Miller, 1956). When creating a new user account, individuals may have several pieces
of information competing for attention, including the primary purpose of creating an
account, work responsibilities, time constraints, personal goals, and so forth (Adams &
Sasse, 1999). Given these other competing cognitions, severity, vulnerability, response
efficacy, and self-efficacy may not be salient to the user, and as such, the user may
default to reusing a password. However, just-in-time warnings can make perceptions of
severity, vulnerability, response efficacy, and self-efficacy more salient, and thereby
more likely to impact behavior. In summary, we propose:
H2. Users who receive a just-in-time fear appeal discouraging password reuse
are more likely to create unique passwords than users who do not receive a just-
in-time fear appeal of password reuse
To test our hypotheses, we conducted an experiment in which participants were
required to create a user account on a website specially constructed for this study. After
the first account creation screen, we randomly manipulated whether a just-in-time fear
appeal was shown and, if so, gave the participant a second opportunity to create a
unique password. We then compared whether users who received a just-in-time fear
appeal created unique passwords more often than users who did not receive a just-in-
time warning. During the account creation and login process, precise timing data for
keystrokes was captured to build a model predicting password reuse.
A total of 148 students from a mid-level information systems course at a large
public university in the South-western United States participated in the experiment.
Participants were compensated with class credit for their participation. Client hardware
and software configuration issues invalidated data from 13 of the participants, leaving
usable data from 135 participants. Students were chosen for our sample because they
commonly have multiple online accounts that may be targets for password reuse. The
students represent an ethnically diverse population at this university, increasing the
generalizability of this study to other nations and cultures. The average amount of
college education was 3.2 years. Fifty-four percent of the participants were male and the
average age was 23.4. The four most represented disciplines for the participants’ majors
were Accounting (15%), MIS (15%), Marketing (13%), and Finance (11%). 55% of the
participants were U.S. citizens, 16% Indian, 10% Mexican, 8% Chinese, and 11%
citizens of other countries.
Experiment Task and Procedure
To participate in the experiment, all participants were required to create a user
account and password on a registration system specially crafted for the experiment
(Figure 1). During the account creation process, participants provided several pieces of
routine information, specifically their name, university email address, and preferred
username. The account creation process also required users to provide a password,
which they self-selected to create uniquely or to reuse an existing password. Precise
keystroke timing information was collected for each field, which was later used to
establish a baseline of keystroke dynamics characteristics. Note that to protect the
privacy of participants, only statistical properties of keystroke dynamics were recorded
and this information was decoupled from personal identifiers.
Figure 1. Account Creation Page
After entering the required information and clicking the “Create Account”
button, participants were assigned randomly to a “warning treatment” (n=69) or a “non-
warning treatment” (n=66). If assigned to the warning treatment, they were shown the
prompt in Figure 2.
We have detected that you may have used this password on other websites. Using the same password for multiple sites puts you at high risk of being hacked.
To protect your privacy, please choose a unique password for this website. Figure 2. Fear Appeal Text
Participants were then prompted to re-enter the same information needed to
create an account, including a password. Participants were not prohibited from entering
the same password that they previously used. The fear appeal message was specifically
crafted to leverage principles from PMT to persuade the participant to create a unique
password. The term “high risk” was included to increase the perceived vulnerability and
the term “hacked” was included to increase perceived severity of reusing a password.
The last sentence in the prompt explains how to protect against hackers by creating a
unique password, thus increasing efficacy. After completing the account creation
process successfully, users in both conditions were presented with a login screen where
they were prompted to provide the username and password they had just created to
access the system. After participants logged in, they were presented with a brief survey
as described below.
In a post-experiment survey, PMT constructs—perceived severity, perceived
vulnerability, response efficacy, and self-efficacy adapted from Vance et al. (2012)
were captured as a manipulation check. Participants were then asked if they created a
unique password when they first attempted to create an account (Figure 3). Finally, for
participants who received the fear appeal treatment, the survey asked if they created a
unique password on their second attempt (Figure 4).
When you FIRST created a password on the account creation page, did you use a password that you have ever used before (e.g., your email password, school password, bank, iTunes, computer password, etc.)? PLEASE ANSWER THIS QUESTION TRUTHFULLY. We are conducting a study about password reuse behaviour. Your privacy is protected, and your password will not be saved. ○ I used a password I have used before when I FIRST created a password in this experiment ○ I created a unique password when I FIRST created a password in this experiment
Figure 3. Password reuse question
After being prompted to create a unique password, did you create a unique password (e.g., a password you have never used before on any account)? ○ Yes ○ No
Figure 4: Password reuse question after fear appeal
To predict password reuse, we captured characteristics of participants’ typing
time (in milliseconds) at which each key was pressed and released. This code was
embedded into the account creation and login pages of the experiment website.
Statistical data collected by the application were stored in a SQL database.
After data collection, algorithms were used to transform the raw keystroke
timing data into digraph patterns and to calculate dwell time, transition time, and typing
speed for each element that was entered (name, email address, username, password, and
password confirmation). Dwell times were calculated by subtracting the time that each
key was pressed from the time it was released. Transition times were calculated by
subtracting the time a key was released from the time the subsequent key was pressed.
Approximately 80% of the transition and dwell times were used in the subsequent
analysis. The remaining 20% of individuals’ transition and dwell times were not used
because they contained substantial corrections which invalidated the keystroke data (i.e.
dozens of corrections or holding down keys for extended periods of time), invalid
characters (a bi-product of some browsers and operating systems), or long pauses
outside 3 standard deviations of the average. Typing speed was then calculated by
dividing the total number of milliseconds each field took to type by the number of
characters entered into the field, then dividing the result by 1000, resulting in a measure
of characters per second.
Data Analysis and Results
Prior to the full data analysis, we assessed the validity and reliability of the
adapted measures. Convergent and discriminant validity of measurement scales were
assessed through a factor analysis using Varimax rotation as well as construct
correlations and cross-correlations. All of the loadings of each item on its latent
construct exceeded 0.6 and loaded less than 0.35 on other constructs. Average variance
extracted (AVE) of all constructs was much larger than 0.5, therefore good convergent
validity was demonstrated (Anderson & Gerbing, 1988). Additionally, all square roots
of AVE exceeded the correlation coefficients between constructs and therefore
demonstrated good discriminant validity (Fornell & Larcker, 1981). Finally, all
Cronbach’s alpha scores were above the 0.7 score suggested by Nunnally (1978).
To ensure that the fear appeals presented to participants elicited the desired
effects based on PMT, we then conducted manipulation checks on perceived severity,
vulnerability, response efficacy, and self-efficacy with an ANOVA. The manipulation
checks supported that the fear appeals influenced perceptions of severity (F (df = 133) =
12.123, p = .001), vulnerability (F (df = 133) = 4.227, p = .043), and response efficacy (F (df
= 133) = 9.131, p = .003). The manipulation did not influence self-efficacy significantly
(F (df = 133) = 1.860, p = .176), possibly because we did not provide training on strategies
for composing unique passwords.
To test hypothesis 1— creating strong unique passwords results in a
measurable difference in keystroke dynamics as compared with typing known
information—we evaluated our data using both statistical analysis of keystroke
dynamics and a support vector machine (SVM) in WEKA, a popular suite of machine
learning software (Hall, et al., 2009).
In our initial analysis of the data, we calculated the average transition time and
dwell time for the three non-password fields together along with the averages, standard
deviations, and z-scores for each of the five fields individually for all users. Deviations
in transition time and dwell time were used to generate features to classify password
reuse. In the first row in Table 2, we show transition data from the first account creation
screen for users who indicated they reused a password. The data indicate that users
typically type their name and email address slightly slower than average (11% and 9%
respectively), and type their username approximately 26% faster than the average of all
non-password fields. The password and password confirmation fields are 35-36%
slower than non-password fields. Fifty-three participants in the fear appeals condition
reported reusing a password initially, but creating a unique password after being
prompted. We conducted a within-subject t-test to examine whether participants’
keystroke dynamics were statistically different when creating unique passwords
compared to reusing passwords. After receiving the prompt to create a new unique
password and again filling out the fields on the account creation page, our results
indicate no statistically significant difference in keystroke dynamics for name (t(df = 52) =
-0.523, p =.603), email address (t(df = 52) = -0.429, p =.670), or username (t(df = 52) = –
0.811, p =.421) for these participants, however, the speed with which the password is
typed drops dramatically from an average of 81ms between key presses to an average of
107ms as shown in Table 2. This within-subject difference between typing non-unique
passwords (first attempt) and typing unique passwords (second attempt) is highly
significant (t (df = 52) = – 3.448, p =.001). Thus, H1 was supported
Table 2: Analysis of transition times
Av er ag e tr an sit io n
tim e (m
s) a nd
% o f
av er ag e
Em ai l A
dd re ss (m
an d % o f a
ve ra ge
U se rn am
s) a nd
of a ve ra ge
Pa ss w or d (m
s) a nd
of a ve ra ge
Pa ss w or d
Co nf irm
at io n (m
an d % o f a
ve ra ge
First Attempt (Non‐unique password)
59 66 (111%)
Second Attempt (Unique Password)
60 65 (109%)
In a supplemental analysis, we built on this initial extraction and analysis of
transition time and dwell time features to create a classification algorithm for
identifying non-unique passwords using a support vector machine in WEKA. A support
vector machine is a supervised learning approach that constructs a set of hyperplanes in
a high-dimensional space. It uses a linear model to implement nonlinear class
boundaries through mapping input vectors into the high-dimensional feature space. In
this feature space, an optimal separating hyperplane is constructed. This hyperplane
gives the maximum separation between decision classes. The training examples that are
closest to the maximum margin hyperplane are called support vectors (Cristianini &
Before applying the support vector machine learning approach, we performed
feature selection using Classifier Subset Evaluator for WEKA’s support vector machine
implementation—SMO. We used a best first search method to select features based on
the raw values, averages, standard deviations, and z-scores of transition time and dwell
time for each field. After selecting the attributes, we created a classification model by
applying WEKA’s SMO classifier to the data. The results were validated using 10-fold
cross validation, and are shown in Table 3.
Table 3: Support vector machine classification results
Class True Positive Rate1
False Positive Rate2
Precision3 Recall4 F-Measure5
Created Unique Password
0.811 0.178 0.789 0.811 0.800
0.822 0.189 0.841 0.822 0.831
1. The fraction of users correctly classified as a hit (i.e., as creating a unique password or reusing a password) 2. The fraction of users incorrectly classified as a hit 3. The fraction of instances classified as a hit that are actually a hit 4. The fraction of hits that are classified as a hit. 5. The harmonic mean of precision and recall (a combined statistic of precision and recall)
In summary, we were able to successfully distinguish between users who
created unique passwords and users who reused passwords based on keystroke
dynamics. Our model correctly classified 81.71% of participants.
To test hypothesis 2—users who receive a just-in-time fear appeal deterring
password reuse are more likely to create unique passwords than users who do not
receive a just-in-time fear appeal of password reuse—we performed a between-subjects
t-test comparing the percentage of people who created unique passwords in the non-fear
appeal group to the percentage of people who created unique password in the fear
appeal group. This test was performed using the “two sample t-test between percents”
from the StatPac Statistics Calculator. Table 4 summarizes the percentages of unique
passwords in each group. The difference between the two groups was highly significant
(t (df = 133) = 7.874, p < .001); hence, H2 was supported.
Table 4. Summary of unique passwords in manipulations
Received fear appeal
# of participants
# of participants who created a unique password
% of participants who created unique passwords
No 66 3 4.45% Yes 69 61 88.41%
This article addressed two research questions. The first research question asked
whether keystroke dynamics could be used to predict if people are reusing passwords.
To answer this research question, we monitored keystroke dynamics—specifically
transition time and dwell time—on the account creation page of a website. We found
significant differences in transition times between unique and non-unique passwords.
We also trained a support vector machine and were able to correctly classify password
reuse with an overall accuracy rate of 81.71%. Hence, we conclude that H1 was
supported and that it is possible to detect password reuse by monitoring users’ keystroke
Our second research question asked whether providing just-in-time fear appeals
would help reduce password reuse. In our study, approximately half of the participants
were given a just-in-time fear appeal discouraging them from reusing passwords. The
fear appeal was created based on constructs found in PMT, and significantly influenced
perceptions of threat severity, threat vulnerability, and response efficacy. After
receiving the fear appeal, 88.41% of participants created unique passwords. Conversely,
in the group that did not received the fear appeal, only 4.45% of participants created
unique passwords. The difference between the two groups was highly significant,
supporting H2. We conclude that just-in-time fear appeals decrease password reuse.
Implications for Research
This paper makes several important contributions to research. First, our research
highlights the need to understand and mitigate password reuse. In our data collection,
only 7 out of 135 participants (5.19%) created a unique password during their first
interaction with our system. This illustrates the prevalence of password reuse. Few
extant studies have examined how to alleviate password reuse, especially in situations
where users may have little or no opportunity for formal cybersecurity education and
shared computer use is high—as is the case in developing countries. Our research helps
address this need by explaining how keystroke dynamics can be diagnostic of password
reuse and how just-in-time fear appeals can decrease password reuse all within the
account creation page of a website.
To our knowledge, this is the first study to examine how keystroke dynamics
can be used to identify password reuse. Although considerable research has been
conducted with regard to keystroke dynamics as a supplement to traditional
authentication mechanisms, existing research examining changes in keystroke dynamics
as a proxy for cognitive changes is scarce. In our study, we show that changes in
keystroke dynamics are indicative of password reuse, and theoretically explain why
differences in keystroke dynamics may result from changes in a user’s cognitive
processing and level of cognitive load. Building on this theory, we were able to
construct an algorithm for identifying password reuse with an accuracy rate of 81.71%.
Future research should examine whether other forms of insecure behaviour can be
detected through changes in keystroke dynamics.
Finally, we contribute to theory by extending PMT to the context of password
reuse. We found that just-in-time fear appeals to avoid password reuse promote unique
password creation. We theorize that this effect is due to the increased salience of the
message due to the immediacy of the fear appeal. Rarely is the act of ‘being secure’ the
primary purpose of using a computer; rather, computers are used to achieve other goals
such as increasing productivity, communicating, socializing, entertainment, and more.
These other goals compete for the user’s attention, and cybersecurity beliefs, such as the
severity and vulnerability of a threat, are often overcome by these other motives. Just-
in-time fear appeals may make cybersecurity beliefs, such as those found in PMT, more
salient, thereby decreasing password reuse.
Implications for Practice
Password reuse is pervasive. Even the most technically-sound system can be
breached by stolen credentials when individuals reuse passwords across multiple
accounts. The detrimental effects of password reuse are particularly difficult to combat
in developing countries in which systems may lack adequate cybersecurity controls,
individuals often use public computers that limit the applicability of password
management systems, government programs might not be in place to enforce
cybersecurity laws, and public cybersecurity education is either scarce or non-existent.
To protect individuals against password reuse, we propose a cost-effective
method to deter password reuse. Our tool focuses on changing users’ password creation
behaviour through two components. First, the system detects when password reuse is
present. The prediction algorithm monitors keystroke dynamics characteristics on
account creation pages to detect possible password reuse. When password reuse is
detected, we found that a simple just-in-time fear appeal can be used to change users’
password creation behaviour, increasing the use of unique passwords in this study from
4.45%, when no fear appeal is used, to 88.41% when a just-in-time fear appeal is
Limitations and Future Research
Future research should examine how just-in-time fear appeals influence user
satisfaction, especially for users who are falsely accused (false-positives). Because of
the small percentage of participants who created unique passwords prior to receiving the
fear appeal in our study (7 out of 135, only 3 of which were in the fear appeal
condition), we did not have adequate statistical power to compare the satisfaction of
users who unjustly received the fear appeal and people who rightfully received the fear
appeal. Based on expectation-confirmation theory (Oliver, 1977), we predict that users
who unjustly receive a fear appeal will experience a decrease in satisfaction with the
system. Expectation-confirmation theory explains that confirming or disconfirming an
individual’s expectations will influence satisfaction. If perceived performance of the
system falls short of an individual’s expectations, this negative disconfirmation will
decrease satisfaction. Ultimately, the level of satisfaction or dissatisfaction experienced
by the user will influence their tendency to use, repurchase, return, or discontinue the
use of a product or service (Lowry, et al., 2009; McKinney, Yoon, & Zahedi, 2002). We
predict that users who unjustly receive a fear appeal to create a unique password will
experience a negative disconfirmation and view the website as less usable, which will
result in a decrease in satisfaction. Future research should validate this proposition.
Future research should also seek to improve the classification accuracy of
password reuse. Although 81.71% is promising, we believe significant improvement
can be made. One possible feature to further improve the algorithm’s accuracy is
through more sophisticated keystroke dynamics analysis, in which digraph patterns for
unique and non-unique password are compared against larger sets of routine and non-
routine text. This approach would require the collection of significantly more text than
is typically available during the account creation process, but could be easily
incorporated into other studies using keystroke dynamics.
Furthermore, future research should cross-validate the results of this study in a
developing country. Our student-based sample consists of individuals who are largely
computer literate and mostly competent typists, which may limit the generalizability of
our findings. Typing fluency may vary across different samples especially in developing
countries, which may influence the features that predict password reuse. However,
research has suggested that despite potential large differences between users (e.g., in
terms of typing fluency, computer skills, and language), the keystroke dynamic
differences within users will likely be constant (Gunetti & Picardi, 2005; Gunetti,
Picardi, & Ruffo, 2005). Future research should validate the generalizability of our
findings, and identify which features are most diagnostic in various populations.
Finally, our research only examined whether users’ passwords were unique. We
did not examine the overall strength of the password, nor did we investigate the pros
and cons of different password creation strategies. It is possible that having more
controls (e.g., requiring users to create unique passwords) can cause users to create
passwords with lower password entropy as a negative side effect (Jenkins, et al., 2010).
Thus, future research should more comprehensively examine how extensively password
reuse should be discouraged (e.g., for all accounts or only for highly sensitive accounts)
and how prompting users to create unique password may influence the entropy of
passwords they create. Future research should also investigate how different password-
creation strategies can encourage both password strength and uniqueness.
This paper helps address the need to reduce password reuse. Password reuse
presents a security risk because a single stolen password can be used to gain
unauthorized access a wide range of important websites and systems. In this study, we
theoretically explain why password reuse can be detected through monitoring keystroke
dynamics on account creation pages. We also proposed that providing just-in-time fear
appeals when violations are detected will decrease password reuse by making important
cybersecurity beliefs more salient. We tested our hypotheses experimentally and found
that keystroke dynamics are diagnostic of password reuse. Keystroke dynamics of users
who created unique passwords were significantly different than the keystroke dynamics
of users who reused passwords. Using this knowledge, we created a support vector
machine capable of identifying password reuse with 81.71% accuracy. We also found
that just-in-time fear appeals strongly decreased password reuse; 88.41% of people who
received a just-in-time fear appeal created unique passwords whereas only 4.45% of
people who did not receive a fear appeal created a unique password. This paper
demonstrates that keystroke dynamics can be used to assess complex human behaviour
and cognitive processing, and that just-in-time fear appeals are a highly influential and
cost-effective way to decrease password reuse. Developing countries may be able to
realize gains in information systems cybersecurity by leveraging the minimally-invasive
and cost-effective methods described in this paper.
Adams, A., & Sasse, M. A. (1999). Users are not the enemy. Communications of the ACM, 42(12), 40-46.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
Biddle, R., Chiasson, S., & Van Orschot, P. C. (2012). Graphical passwords: Learning from the first twelve years. ACM Computing Surveys, 44(4), 19:11-19:41.
Bonneau, J., Herley, C., van Oorschot, P. C., & Stajano, F. (2012, May 20-23). The quest to replace passwords: A framework for comparative evaluation of web authentication schemes. Paper presented at the 2012 IEEE Symposium on Security and Privacy, San Francisco, CA.
Brechbuhl, H., Bruce, R., Dynes, S., & Johnson, M. E. (2010). Protecting critical information infrastructure: Developing cybersecurity policy. Information Technology for Development, 16(1), 83-91.
Bryant, K., & Campbell, J. (2006). User behaviours associated with password security and management. Australasian Journal of Information Systems, 14(1), 81-100.
Campbell, J., Kleeman, D., & Ma, W. (2007). The good and not so good of enforcing password composition rules. Information Systems Security, 16(1), 2-8.
Campbell, J., Ma, W., & Kleeman, D. (2011). Impact of restrictive composition policy on user password choices. Behaviour & Information Technology, 30(3), 379- 388.
Carte, T. A., Dharmasiri, A., & Perera, T. (2011). Building IT capabilities: Learning by doing. Information Technology for Development, 17(4), 289-305.
Charoen, D., Murali, R., & Lorne, O. (2008). Improving end user behaviour in password utilization: An action research initiative. Systemic Practice & Action Research, 21(1), 55-72.
Chen, Y. N., Chen, H. M., Huang, W., & Ching, R. K. H. (2006). E-government strategies in developed and developing countries: An implementation framework and case study. Journal of Global Information Management, 14(1), 23-46.
Cone, B. D., Irvine, C. E., Thompson, M. F., & Nguyen, T. D. (2007). A video game for cyber security training and awareness. Computers & Security, 26(1), 63-72.
Crano, W. D. (1995). Attitude strength and vested interest. In R. E. Petty & J. A. Krosnick (Eds.), Attitude Strength: Antecedents and Consequences (pp. 131– 158). Mahwah, NJ, USA: Erlbaum.
Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines And Other Kernel-Based Learning Methods. Cambridge, UK: Cambridge University Press.
Crump, M. J. C., & Logan, G. D. (2010). Hierarchical control and skilled typing: Evidence for word-level control over the execution of individual keystrokes. Journal of Experimental Psychology-Learning Memory and Cognition, 36(6), 1369-1380.
Florencio, D., & Herley, C. (2006). How to login from an internet cafe without worrying about keyloggers. Paper presented at the Symposium on Usable Privacy and Security.
Florencio, D., & Herley, C. (2007). A large-scale study of web password habits. Paper presented at the Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, Canada.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Furnell, S. (2007). An assessment of website password practices. Computers & Security, 26(7-8), 445-451.
Gaines, R. S., Lisowski, W., Press, S. J., & Shapiro, N. (1980). Authentication by keystroke timing: Some preliminary results: RAND Corporation.
Gaw, S., & Felten, E. W. (2006). Password management strategies for online accounts. Paper presented at the Proceedings of the Second Symposium on Usable Privacy and Security.
Gentner, D. R., Grudin, J., & Conway, E. (1980). Finger movements in transcription typing.
Gunetti, D., & Picardi, C. (2005). Keystroke analysis of free text. ACM Transactions on Information and System Security (TISSEC), 8(3), 312-347.
Gunetti, D., Picardi, C., & Ruffo, G. (2005). Keystroke analysis of different languages: A case study. Advances in Intelligent Data Analysis, 6(1), 133-144.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1), 10-18.
Herath, T., & Rao, H. R. (2009). Protection motivation and deterrence: A framework for security policy compliance in organisations. European Journal of Information Systems, 18(2), 106-125.
Huang, C.-Y., Ma, S.-P., & Chen, K.-T. (2011). Using one-time passwords to prevent password phishing attacks. Journal of Network & Computer Applications, 34(4), 1292-1301.
Hwang, M.-S., Chong, S.-K., & Chen, T.-Y. (2010). DoS-resistant ID-based password authentication scheme using smart cards. Journal of Systems & Software, 83(1), 163-172.
Ives, B., Walsh, K. R., & Schneider, H. (2004). The domino effect of password reuse. Communications of the ACM, 47(4), 75-78.
Jenkins, J. L., Durcikova, A., Ross, G., & Nunamaker Jr, J. F. (2010, December 12-15). Encouraging users to behave securely: Examining the influence of technical, managerial, and educational controls on users’ secure behavior. Paper presented at the International Conference on Information Systems, Saint Louis, Missouri.
John, B. E. (1996). TYPIST: A theory of performance in skilled typing. Human- Computer Interaction, 11(4), 321-355.
Johnston, A. C., & Warkentin, M. (2010). Fear appeals and information security behaviors: An empirical study. MIS Quarterly, 34(3), 549-565.
Joyce, R., & Gupta, G. (1990). Identity authentication based on keystroke latencies. Communications of the ACM, 33(2), 168-176.
Keith, M., Shao, B., & Steinbart, P. (2009). A behavioral analysis of passphrase design and effectiveness. Journal of the Association for Information Systems, 10(2), 63- 89.
Larochelle, S. (1983). A comparison of skilled and novice performance in discontinuous typing. In W. E. Cooper (Ed.), Cognitive Aspects of Skilled Typewriting (pp. 67– 94). New York, NY, USA: Springer-Verlag.
Leggett, J., & Williams, G. (1988). Verifying identity via keystroke characteristics. International Journal of Man-Machine Studies, 28(1), 67-76.
Logan, G. D. (2003). Simon-type effects: Chronometric evidence for keypress schemata in typewriting. Journal of Experimental Psychology-Human Perception and Performance, 29(4), 741-757.
Logan, G. D., & Crump, M. J. C. (2009). The left hand doesn’t know what the right hand is doing: The disruptive effects of attention to the hands in skilled typewriting. Psychological Science, 10(1), 1296–1300.
Lowry, P. B., Romano, N. C., Jenkins, J. L., & Guthrie, R. W. (2009). The CMC interactivity model: How interactivity enhances communication quality and process satisfaction in lean-media groups. Journal of Management Information Systems, 26(1), 155-195.
Maurer, T. (2011). Cyber norm emergence at the United Nations—an analysis of the UN’s activities regarding cyber-security: Belfer Center for Science and International Affairs, Harvard Kennedy School.
McKinney, V., Yoon, K., & Zahedi, F. (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296-315.
Miller, G. A. (1956). The magical number seven plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Miller, G. A., Galanter, E., & Pribram, K. H. (1986). Plans and the Structure of Behavior. New York, NY, USA: Adams-Bannister-Cox.
Milne, G. R., Labrecque, L. I., & Cromer, C. (2009). Toward an understanding of the online consumer’s risky behavior and protection practices. Journal of Consumer Affairs, 43(3), 449-473.
Notoatmodjo, G., & Thomborson, C. (2009). Passwords and perceptions. Paper presented at the Proceedings of the Seventh Australasian Conference on Information Security.
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). New York, NY, USA: McGraw- Hill.
Oghenerukevbe, E. A. (2010). Mnemonic passwords practices in corporate sites in Nigerian. Journal of Internet Banking & Commerce, 15(1), 1-11.
Oliver, R. L. (1977). Effect of expectation and disconfirmation on postexposure product evaluations – Alternative interpretation. Journal of Applied Psychology, 62(4), 480-486.
Park, S., Park, J., & Cho, S. (2010, June 29-July 1). User authentication based on keystroke analysis of long free texts with a reduced number of features. Paper presented at the 2010 Second International Conference on Communication Systems, Networks and Applications (ICCSNA).
Pick, J. B., & Azari, R. (2008). Global digital divide: Influence of socioeconomic, governmental, and accessibility factors on information technology. Information Technology for Development, 14(2), 91-115.
Posey, C., Roberts, T. L., Lowry, P. B., Courtney, J., & Bennett, R. J. (2011, September 22–23). Motivating the insider to protect organizational information assets: Evidence from protection motivation theory and rival explanations. Paper presented at the The Dewald Rhoode Workshop in Information Systems Security 2011, Blacksburg, Virginia, USA.
Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology, 91(1), 93-114.
Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A Revised theory of protection motivation. In J. Cacioppo & R. Petty (Eds.), Social Psychophysiology. New York, NY, USA: Guilford Press.
Roztocki, N., & Weistroffer, H. R. (2011). Information technology success factors and models in developing and emerging economies. Information Technology for Development, 17(3), 163-167.
Shaffer, L. H. (1976). Intention and performance. Psychological Review, 83(5), 375- 393.
Shaffer, L. H., & Hardwick, J. (1968). Typing performance as a function of text. Quarterly Journal of Experimental Psychology, 20(4), 360-369.
Shouhong, W., & Hai, W. (2008). Password authentication using Hopfield neural networks. IEEE Transactions on Systems, Man & Cybernetics: Part C – Applications & Reviews, 38(2), 265-268.
Tagert, A. C. (2010). Cybersecurity Challenges in Developing Nations. Carnegi Mellon University, Pittsburgh, PA.
Tam, L., Glassman, M., & Vandenwauver, M. (2010). The psychology of password management: A tradeoff between security and convenience. Behaviour & Information Technology, 29(3), 233-244.
Tappert, C., Villani, M., & Cha, S.-H. (2009). Keystroke biometric identification and authentication on long-text input. In L. Wang & X. Geng (Eds.), Behavioral Biometrics for Human Identification: Intelligent Applications.
Teh, P. S., Teoh, A. B. J., Tee, C., & Ong, T. S. (2010). Keystroke dynamics in password authentication enhancement. Expert Systems with Applications, 37(12), 8618-8627.
Vance, A., Siponen, M., & Pahnila, S. (2012). Motivating IS security compliance: Insights from habit and protection motivation theory. Information & Management, 49(3–4), 190-198.
Vizer, L. M., Zhou, L. N., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886.
Weingarten, R., Nottbusch, G., & Will, U. (2004). Morphemes, syllables, and graphemes in written word production. In T. Pechmann & C. Habel (Eds.), Language Production (pp. 529–572). Berlin, Germany: Mouton de Gruyter.
Will, U., Nottbusch, G., & Weingarten, R. (2006). Linguistic units in word typing: Effects of word presentation modes and typing delay. Written Language & Literacy, 9(1), 153-176.
Workman, M., Bommer, W. H., & Straub, D. (2008). Security lapses and the omission of information security measures: A threat control model and empirical test. Computers in Human Behavior, 24(6), 2799-2816.
Wu, C., & Liu, Y. (2008). Queuing network modeling of transcription typing. ACM Transactions on Computer-Human Interaction, 15(1).
Zhang, L., & McDowell, W. C. (2009). Am I really at risk? Determinants of online users’ intentions to use strong passwords. Journal of Internet Commerce, 8(3/4), 180-197.
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