Keystroke dynamics-based authentication for mobile devices

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    often adopted as the only security mechanism for mobile devices.

    keystroke dynamics-based authentication (KDA) on mobile devices. We found that a KDA

    system can be effective for mobile devices in terms of authentication accuracy. Use of

    artificial rhythms leads to even better authentication performance.

    2008 Elsevier Ltd. All rights reserved.

    d mo

    (from 0000 to 9999). It is much easier for a potential impostor

    a typical year (Kowalski and Goldstein, 2006). Third, we tend to

    lend mobile phones easily to other people, thus they are

    exposed to a higher risk of surreptitious use.

    Recently, biometrics has been proposed to improve the

    security of mobile devices. The term biometrics is defined

    improves the overall reliability of authentication.

    mobile devices, KDA has the following advantages over other

    biometrics-based methods. First, most biometrics-based

    methods require an extra device, e.g. a finger-scanner or an

    iris-scanner (Clarke and Furnell, 2005), which restricts

    mobility as well as increases cost. On the other hand, KDA

    * Corresponding author. Tel.: 82 2 880 6275; fax: 82 2 889 8560.ac.kr (S. Cho), shpark82@snu.ac.kr (S. Park).

    ava i lab le at www.sc ienced i rec t . com

    .e

    c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 3E-mail addresses: hss9414@snu.ac.kr (S.-s. Hwang), zoon@snu.to acquire the password by shoulder surfing and systematic

    trial-and-error attacks. Second, mobile devices may be easily

    lost or stolen because of their small sizes. For example, more

    than one million mobile phones are stolen in Europe for

    Keystroke dynamics-based authentication (KDA) is one of

    biometrics-based authentication methods, motivated by the

    observation that a users keystroke patterns are consistent

    and distinct from those of other users.When implemented foret al., 2008). Cell phones and personal digital assistants (PDA)

    are used for banking and stock trading nowadays. However,

    there are three reasons why security of mobile devices has

    a lot to be desired. First a PIN comprises only four digits, thus,

    the number of candidate passwords is limited to only 10,000

    ical attribute such as a fingerprint, a face and an iris, whereas

    behavioral approaches utilize some characteristic behavior,

    such as the way we speak or sign our name (Clarke and Fur-

    nell, 2005). Clarke and Furnell (2007a) concluded that the two-

    factor authentication, combining PIN code and biometrics,Keywords:

    Mobile device

    Keystroke dynamics

    Artificial rhythms

    Tempo cues

    Biometrics

    User authentication

    1. Introduction

    Use of mobile devices is diversifie0167-4048/$ see front matter 2008 Elsevidoi:10.1016/j.cose.2008.10.002re and more (Chen

    by International Biometric Group as the automated use of

    physiological or behavioral characteristics to determine or

    verify identity. Physiological biometrics relies upon a phys-2 June 2008

    Accepted 29 October 2008Because of their limited length, PINs are vulnerable to shoulder surfing and systematic

    trial-and-error attacks. This paper reports the effectiveness of user authentication usingReceived in revised form tion number (PIN) isArticle history:

    Received 26 November 2007

    Recently, mobile devices are used in financial applications such as banking and stock

    trading. However, unlike desktops and notebook computers, a 4-digit personal identifica-Keystroke dynamics-basedevices

    Seong-seob Hwang, Sungzoon Cho*, Sungho

    Seoul National University, 599 Gwanangno, Gwanak-gu, Seoul 151-

    a r t i c l e i n f o a b s t r a c t

    journa l homepage : wwwer Ltd. All rights reservedauthentication for mobile

    Park

    , Republic of Korea

    l sev ie r . com/ loca te /cose.

  • quality. Another measure of data quality is how unique the

    employed novelty detection framework where only the valid

    users patterns are used for training. Second, each user in their

    experiments enrolled 30 typing patterns. In practice, users

    would not endure such a long enrollment procedure. More-

    over, the typing speed on mobile devices is much slower than

    that on a local PC. In our study, we collected only five patterns

    from each user for enrollment. We compensated the reduced

    data quantity with improved data quality through use of

    artificial rhythms and cues strategy. Third, they utilized

    various patterns such as 4-digit PINs, 11-digit telephone

    numbers, and text messages while we focused only on 4-digit

    PIN since PIN has been fixed to four digits for decades. Fourth,

    c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 386typing patterns are. The more unique, the less likely the

    patterns are similarly replicated by impostors. Recently, arti-

    ficial rhythms and tempo cues were proposed to improve the

    quality of typing patterns: uniqueness and consistency in

    particular (Cho and Hwang, 2006). Improving the data quality

    by decreasing variability and increasing uniqueness helps us

    alleviate the weakness of a short PIN.

    In this paper, we propose KDA with artificial rhythms and

    tempo cues for mobile user authentication. To compare

    between Natural Rhythm without Cue and Artificial

    Rhythms with Cues, we completed the following tasks. First,

    we implemented KDA system on a mobile phone which is

    connected to a remote server through a wireless network. The

    novelty detector classifier was built since only valid users

    patterns are available in practice. Second, subjects were asked

    to perform enrollment, login, and even intrusion to other

    subjects accounts. Whenever a subject types his or her

    password, the typing pattern is collected, sent to a server and

    stored. Third, a comparative analysis was conducted to verify

    the superiority of artificial rhythms and cues over natural

    rhythms without cues. We also tested hypotheses to compare

    the performance involving different typing strategies.

    The organization of this paper is as follows. The following

    section introduces keystroke dynamics-based authentication

    for mobile devices and describes our methods to improve the

    quality of typing patterns. Section 3 presents the data

    collected and experimental results. Finally, conclusions and

    a list of future work are discussed in Section 4.

    2. Keystroke dynamics-based authenticationfor mobile devices

    2.1. Keystroke dynamics-based authentication (KDA)

    The password-based authentication is the most commonly

    used in identity verification. However, it becomes vulnerable

    when the password is stolen. Keystroke dynamics-based

    authentication was proposed to provide additional security

    (Gaines et al., 1980; Umphress and Williams, 1985). Keystroke

    dynamics-based authentication (KDA) is to verify a users

    identity using not only the password but also keystroke

    dynamics. For example, a keystroke pattern is transformedrequires no additional device. Second, users tend to be reluc-

    tant to provide their fingerprints or irises. On the other hand,

    a user always has to type his or her password to log in, so

    collecting keystroke patterns can be donewithout causing any

    extra inconvenience to the user. Third, a scanned fingerprint

    or iris requires a large volume of memory, a higher computing

    power and communication bandwidth than keystroke timing

    vectors. The efficiency of KDA is particularly important in

    mobile environment which tends to have a smaller memory,

    a lower computing power and slower wireless Internet than

    a PC on the wired Internet.

    Behavioral attributes are more subject to deviation from

    norms than physical ones. A high variability leads to a high

    authentication error. The variability is a measure of datainto a timing vector when a user types a string 5805 as

    illustrated in Fig. 1. The duration and interval times aremeasured by milliseconds. A user can get access only if his

    timing vector is similar enough to those already registered in

    the server. Thus, he or she can only get access if the password

    is typed with the correct rhythm.

    Three steps are involved in KDA as illustrated in Fig. 2.

    First, a user enrolls his/her keystroke patterns. A keystroke

    pattern is defined as depicted in Fig. 1. A password of m

    characters is transformed into a (2m 1)-dimensional timingvector. A duration denotes a time period during which a key

    is pressed while an interval is a time period between

    releasing a key and stroking the next key. Second, a classifier

    is built using the keystroke patterns. The classifier, in a sense,

    is a prototype of the valid user patterns. Third, when a new

    keystroke pattern is given, one will reject it as an impostor

    pattern if the distance between the prototype and the pattern

    is greater than some threshold, or accept it as the valid users

    pattern otherwise.

    KDA can help us improve security for various services

    involving mobile devices (Hwang et al., 2007). Even when an

    impostor obtains both PIN and the mobile device, KDA can still

    prevent him from logging in through the strengthened

    authentication process. Recently, Clarke and Furnell (2005,

    2007a,b) studied user identification using KDA on mobile

    devices. They utilized the keystroke of 11-digit telephone

    numbers and text messages as well as 4-digit PINs to classify

    users. Their identification models were based on feed forward

    multi-layer perceptron (FF-MLP), radial basis function (RBF)

    networks, andgeneralizedregressionneuralnetworks (GRNNs).

    Our approach is different from that of Clarke and Furnell

    (2005, 2007a,b) in the following aspects. First, they built

    a classifier using impostors patterns aswell as the valid users

    patterns. In reality, however, impostors patterns are not

    available unless the password be disclosed to potential

    impostors and their patterns are collected. Rather, we

    Fig. 1 A keystroke pattern is transformed into a timing

    vector when a user types a string 5805. The duration and

    interval times are measured by milliseconds.their subjects used an SW interface developed on a laptop

    while our subjects used a real mobile phone, which is a third

  • en

    c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 3 87generation synchronized IMT-2000 cellular system

    (CDMA2000 1xEV-DO) (Qualcomm).

    2.2. Improving data quality

    One way to cope with the lack of data quantity is to improve

    data quality. Data quality in KDA can be measured in terms of

    uniqueness, consistency, and discriminability (Cho and

    Hwang, 2006). Uniqueness is concerned with how different

    a valid users typing patterns used to build a classifier are from

    those of potential impostors. Also, consistency is concerned

    with how similar a valid users access typing patterns are to

    his enroll typing patterns. Finally, discriminability is con-

    cerned with how well access typing patterns and impostor

    typing patterns could be separated. The definition of

    discriminability implies that two possible approaches exist to

    improve discriminability. The first is to improve uniqueness,

    and the second is to improve consistency.

    Fig. 2 Three steps of KDA framework: enrollmAs oneway to improve uniqueness, it has been proposed to

    type a password with artificial rhythms reproducible by the

    valid user only (Cho and Hwang, 2006). Table 1 represents

    various artificial rhythms to increase typing uniqueness. In

    this paper, pauses are selected among various artificial

    rhythms since they are simple and easy to control. A user

    inserted a number of intervals where deemed necessary to

    make the timing vector unique. As shown in Fig. 3, 5805 can

    be typed as 5_ _ _80_ _5 with a three beat long pause between

    5 and 8, and another two beat long pause between 0 and 5.

    There are many combinations of inserting pauses in terms of

    Table 1 Various artificial rhythms.

    Artificial Rhythms Advantages

    Pauses Flexible

    Musical rhythm Consistent, Easy to remember

    Staccato Consistent

    Legato Consistent

    Slow tempo Flexiblethe positions and lengths of pauses. The more combinations

    there are, the harder an impostor can guess it correctly.

    In order to prevent pauses from being inconsistent, tempo

    cues are provided (Cho and Hwang, 2006). Tempo cues (Fig. 6)

    work like a metronome helping the user keep the beat. Given

    the tempo beat, the user only needs to remember the number

    of beats for each pause. Usually, they can be provided in three

    modes: auditory, visual, and audio-visual. In addition, users

    are allowed to choose the tempo of the cue. It has another

    advantage of improving uniqueness since only the valid user

    knows the tempo.

    Fig. 3 presents the timing vectors of password 5805 from

    strategies Natural Rhythm without Cue (Fig. 3a) and Arti-

    ficial Rhythms with Cues (Fig. 3b). The dotted lines represent

    the enroll patterns, x, while the solid line represents the

    prototype, m. Note that the timing vectors depicted in Fig. 3

    were normalized, or divided by the two-norm. When

    comparing timing vectors between strategies, there are

    t, classifier building, and user authentication.differences in terms of both uniqueness and consistency.

    First, observe the intervals between 5 and 8 from Artificial

    Rhythms with Cues are very large compared to those from

    Natural Rhythm without Cue. An impostors pattern would

    be more similar to those from Natural Rhythmwithout Cue

    and it is highly likely to be distinct from those from Artificial

    Rhythms with Cues. Same can be said for intervals between

    0 and 5. Thus, long intervals improve uniqueness of a users

    patterns. Second, observe that the differences between the

    enroll patterns and the prototype are smaller from Artificial

    Rhythms with Cues than from Natural Rhythm without

    Disadvantages Remedies

    Inconsistent when long Use of cues

    Rhythmical sense required

    Limited

    Limited, Exact duration Use of cues

    Inconsistent Use of cues

  • participated in our experiment in July 2006. In the experiment,

    Fig. 3 Timing vectors of a password 5805.

    c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 388Cue. Tempo cues improved the consistency of the patterns

    from Artificial Rhythms with Cues.

    2.3. Mobile application

    The experiments were performed on the third generation

    synchronized IMT-2000 cellular system (CDMA2000 1xEV-DO)

    (Qualcomm). The mobile device used is SAMSUNG SCH-V740

    (Korean model number; Samsung Electronics website) as

    shown in Fig. 4. The software authentication module was

    implemented in WIPI (wireless Internet platform for interop-

    erability), developed by the Mobile Platform Special Subcom-

    mittee of the Korea Wireless Internet Standardization Forum

    (KWISF). These are standard specifications necessary for

    providing an environment for mounting and implementing

    applications downloaded via the wireless Internet on the

    mobile communication terminal. For more details, see the

    WIPI website.

    Any user authentication including KDA has two types of

    error, i.e. false acceptance rate (FAR) and false rejection rate

    (FRR) (Golarelli et al., 1997). One type of error can be reduced at

    the expense of the other by varying a threshold. Thus, in order

    to avoid effects of arbitrary threshold selection, the modelswere compared in terms of the equal error rate (EER) where

    Fig. 4 Mobile phone used in the exa 4-digit numeric PIN was used. Two strategies were

    employed: Natural Rhythm without Cue and Artificial

    Rhythms with Cues. The same password for each user was

    used in both strategies. Each user enrolled five typing patterns

    for each strategy. After enrollment, each user made 30 login

    attempts using each strategy. Users were also given pass-

    words of other users and told to act as impostor to those

    passwords, i.e., typing it twice each. Since there are 24 otherthe FRR and the FAR are equal. In practice, a threshold has to

    be decided empirically. For a more detailed discussion of

    proper threshold selection, see Fawcett (2006). Without KDA,

    an impostor could login as a valid user if he knows the pass-

    word, FAR 100% results. On the other hand, the valid userwill always be able to log in, which corresponds to FRR 0%,i.e., FAR 100% and FRR 0%.

    3. Performance evaluation

    3.1. Data collection

    A total of 25 users aged from 22 to 33 (the average is 25.3)users, each user typed passwords 48 times. In summary, for

    periment: SAMSUNG SCH-V740.

  • c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 3 89each password, we collected five enroll typing patterns, 30

    legitimate access typing patterns, and 48 impostor typing

    patterns.

    The data above were collected from a scenario involving

    a virtual stock exchange (Fig. 5). A user designs ones own

    artificial rhythm (Fig. 3) and chooses the type of tempo cues

    (Fig. 6). The tempo of the cue was fixed to 500 ms for

    convenience.

    All users were asked the reason why a particular password

    was chosen (Table 2). There are three different kinds of

    reasons (see the fourth column of Table 2) for selecting

    a password. First, familiar numbers were chosen such as

    favorite combination, birth date, or telephone number.

    Second, numbers that are easy to remember were selected.

    For instance, both users 09 and 19 chose 2580 because that

    is an easy number for themalthoughwith different reasons.

    The number keys used in 2580 are located in the middle

    column of a keypad on the mobile phone, so it is easy to type.

    2580 is also the title of a very popular TV investigative show

    in Korea, similar to 60 Minutes in the US. Thus, it is easy to

    remember. Third, certain passwords were chosen for no

    particular reason at all. Of all users, 44% indicated Famil-

    iarity, and 32% indicated Ease, while only 24% indicated

    Randomness. This clearly suggests that introduction of

    artificial rhythms and tempo cues could enhance security.

    A PIN has been fixed to 4-digits for decades and the number of

    Fig. 5 User interface for acandidate passwords used for the mobile handset is only

    10,000 (from 0000 to 9999). It is not difficult to guess a PIN

    because an impostor might know the owners birth date or

    telephone number, and a PIN easy for one person to type

    would be also easy for another to type. For Typing Hands,

    (see the fifth column of Table 2), 68% indicated both hands

    while 32% indicated one hand. This implies that each user

    might have a particular way to type on a mobile device as on

    a keyboard.

    3.2. Experimental results

    We introduced artificial rhythms and cues to improve data

    quality. Thus, we have to show from experiments that the

    quality actually improved. Hwang et al. (submitted for publi-

    cation) showed that typing patterns from Artificial Rhythms

    with Cues were significantly more unique and consistent

    than those patterns from Natural Rhythm without Cue.

    Thus, we instead here show that the authentication accuracy

    improves.

    Table 3 presents the authentication results from two

    strategies Natural Rhythm without Cue and Artificial

    Rhythms with Cues. Out of 25 users, 19 users EER decreased

    19% on average while six users EER increased 4% on average.

    Four users EER decreased to zero. Especially, the EERs of user

    03 and 14 were dramatically decreased, both from 40% to 0%

    virtual stock exchange.

  • Table 2 User passwords and answers to questionnaire(R[ randomness, F[ familiarity, E[ ease).

    User Age Password Selectionreason

    Use ofhand(s)

    Elapsed time(naturalrhythm)(ms)

    01 23 1223 R Both 1163

    02 24 3143 R Both 832

    03 23 0083 F (favorite #) Both 1408

    04 23 1472 F (favorite #) Both 1017

    05 28 7118 F (phone #) E One hand 89706 23 7265 R Both 921

    07 30 2385 F (phone #) Both 812

    08 25 5805 F (phone #) Both 1442

    09 24 2580 F (favorite #) E One hand 101310 28 3784 R One hand 1755

    11 24 3579 F (a sequence

    of odd #)

    One hand 1069

    12 22 1379 E Both 671

    13 25 0822 R One hand 1357

    c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 390and 34% to 0%, respectively. The overall EER decreased from

    13% to 4% by using Artificial Rhythms with Cues.

    Fig. 7 shows a detailed picture of what really happened.

    First, note that the classifier in our study is a very simple

    distance based one. A prototype of a user enroll patterns is

    calculated and stored. When a new keystroke pattern is pre-

    sented, the distance between the pattern and the prototype is

    computed. If it is small enough, access is granted. If not, it is

    not granted. In order to gain good authentication perfor-

    mance, three conditions have to be met. First, enroll patterns

    have to be consistent, or the enroll distances between the

    prototype and the enroll patterns have to be small. Second,

    login patterns have to be close to the enroll prototype, or the

    login distances between the enroll prototype and the login

    patterns have to be small. Third, enroll patterns have to be

    unique, or the impostor distances between the enroll

    prototype and impostor patterns have to be large better. User

    03 reduced EER dramatically through use of Artificial

    Rhythms and Cues. Thus, we show in Fig. 7 the cumulative

    distributions of the three kinds of distances, enroll, login,

    and impostor. In (a), login distances (black) are larger than

    enroll distances (blue), which means the users login patterns

    are somewhat different from the enrolled patterns. The real

    reason for user 3s large error comes from the fact that

    impostor distances are not large (red). Now see how these

    Fig. 6 Various tempo cues.14 27 4569 R Both 1276

    15 23 0203 F (birth date) Both 1222

    16 24 1004 R Both 794

    17 24 5472 R Both 2151

    18 23 3887 F (privacy) One hand 792

    19 28 2580 E Both 906

    20 23 2220 E One hand 870

    21 33 1133 E Both 675

    22 25 1258 F (phone #) One hand 1105three lines change in (b). Both login and enroll distances are

    very small while impostor distances are quite large. This

    separation of login distances from impostor distances

    accounts for perfect discrimination between legitimate user

    and impostors.

    Recently, Hwang et al. (submitted for publication) found

    that artificial rhythms and cues were particularly useful to

    23 27 5262 E Both 1020

    24 30 1125 E Both 739

    25 24 0305 F (birth date) Both 632

    Table 3 The equal error rate (%) from two strategies.

    User NaturalRhythmwithoutCue

    ArtificialRhythmwithCues

    User NaturalRhythmswithoutCue

    ArtificialRhythmswithCues

    User 01 14 0 User 15 18 4

    User 02 0 3 User 16 6 3

    User 03 40 0 User 17 8 11

    User 04 15 2 User 18 6 4

    User 05 0 4 User 19 30 3

    User 06 16 3 User 20 4 3

    User 07 4 0 User 21 12 15

    User 08 18 2 User 22 28 8

    User 09 6 3 User 23 8 4

    User 10 5 3 User 24 21 2

    User 11 18 3 User 25 1 3

    User 12 0 7 Average 13 4

    User 13 23 8 Min 0 0

    User 14 34 0 Max 40 15

  • difference between typing hands. Also, when the users

    employed Artificial Rhythms with Cues, average EER was

    less than 5% for all cases. These results are comparable to

    those reported in Hwang et al. (submitted for publication)

    where authentication accuracy was greatly improved with

    a PC keyboard by employing Artificial Rhythms and Cues.

    We tested hypotheses to compare the performance

    involving different passwords and different typing strategies.

    Specific hypotheses and p-values are summarized in Table 6.

    Only the 1stH1 hypothesiswas acceptedwith p-value of 0.0002

    while all the otherswere rejected. The results indicate that the

    EERs using Artificial Rhythms and Cues clearly decreased

    compared to that using Natural Rhythm without Cue. We

    concluded that the effect of either Password Selection

    Reason or Typing Hands was negligible on the

    Table 5 The average EERs (%) with respect to theproperties involving Password Selection Reason andTyping Hands.

    Section NaturalRhythmwithoutCue

    ArtificialRhythmswith Cues

    Frequency

    Password Familiarity 14 3 11/25

    Selection Ease 10 5 8/25

    Reason Randomness 13 4 8/25

    One hand vs.

    both hands

    One hand 11 4 8/25

    Both hands 14 4 17/25

    c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 3 91poor typists in desktop keyboard environment. We now

    investigate if this is also true in mobile device environment.

    We call a user as a poor typist if his average elapsed time

    with Natural Rhythm without Cue is greater than 1 s or as

    a good typist otherwise. We identified 13 poor typists out of

    25 users. The average EERs with respect to typing ability are

    Fig. 7 Cumulative distributions of enroll (black), login

    (blue), and impostor (red) distances when (a) Natural

    Rhythm without Cue and (b) Artificial Rhythms with

    Cues strategies were employed, respectively.shown in Table 4. For the good typists, the average EER from

    Natural Rhythm without Cue was 8% while that from

    Artificial Rhythmswith Cues was 4%. On the other hand, for

    the bad typists, the average EER from Natural Rhythm

    without Cue was 18% while that from Artificial Rhythms

    with Cues was 4%. Although the poor typists yielded much

    higher error rates when Natural Rhythm without Cue was

    used, they became comparable to the good typists when

    Artificial Rhythms with Cues was used. Clearly, artificial

    rhythms and cues are particularly beneficial to the users with

    a poor typing ability in mobile user authentication.

    Table 5 compares the average EERs for different password

    selection reasons and Typing Hands. For Password Selec-

    tion Reason, the average EER of Ease was the lowest from

    Natural Rhythm without Cue. However, there was little

    difference among password selection reasons. When the

    users employed Artificial Rhythms with Cues, average EER

    was less than 5% for all cases. For Typing Hands, we

    observed essentially the same trend. There was little

    Table 4 The average EER(%) for different typing abilityand strategy.

    Natural Rhythmwithout Cue

    Artificial Rhythmswith Cues

    Good typists 8 4

    Poor typists 18 4Table 6 Hypotheses and p-values involving passwordand typing hand(s).

    Hypothesis H1 hypotheses p-Value

    Typing strategy The average EER involving

    Artificial Rhythms with Cues

    is lower than that involving

    Natural Rhythmwithout Cue.

    0.0002

    For natural rhythms, the

    average EER of Ease is lower

    than that of Familiarity.

    0.2339

    Natural rhythms For natural rhythms, the

    average EER of Ease is lower

    than that of Randomness.

    0.2754

    For natural rhythms, the

    average EER of Familiarity is

    lower than that of

    0.4576Randomness.

    For artificial rhythms, the

    average EER of Ease is lower

    than that of Familiarity.

    0.1243

    Artificial rhythms For artificial rhythms, the

    average EER of Ease is lower

    than that of Randomness.

    0.3075

    For artificial rhythms, the

    average EER of Familiarity is

    lower than that of

    Randomness.

    0.2636

    Typing hand For Typing Hand(s),

    Artificial Rhythms with Cues

    are beneficial to users who

    typed using both hands.

    0.2409

    A bold figure indicates an accepted hypothesis.

  • length and alphabet, PINs are susceptible to shoulder surfing

    and systematic trial-and-error attacks. This paper investi-

    c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 392gated the effectiveness of user authentication using keystroke

    dynamics-based authentication (KDA) on mobile devices. In

    particular, we utilized artificial rhythms and tempo cues to

    overcome problems resulting from short PIN length. Through

    the experiments involving human subjects, we found that the

    proposed strategy reduced the error from 13% to 4%.

    A few limitations and future directions need to be

    addressed. First, comparison research for various mobile

    devices is needed to enhance the usability of KDA. Second, we

    have to apply to a more diverse group of users. Althoughmost

    people make use of mobile devices, various usage-patterns

    may exist. Third, we measured performance in terms of EER.

    Thus, the error rates presented in the paper should be takenauthentication. It was found from the results that the use of

    Artificial Rhythmswith Cues improves the accuracy for user

    authentication.

    Table 7 compares the performance with related works. The

    experiments of Clarke and Furnell (2005, 2007a,b) involving 4-

    digit PINs resulted in EERs ranging from 9% to 16%. When the

    users adopted the Natural Rhythm without Cue, we

    obtained the EER of 13%, which is similar to the ones from

    Clarke and Furnell. When they employed Artificial Rhythms

    with Cues, however, we found that the error was reduced to

    3%. Given the very small number of patterns for training (or

    validation), we found that Artificial Rhythms with Cues did

    improve authentication accuracies significantly.

    4. Discussion and conclusions

    For decades, the mobile environment has stabilized with

    stunning speed. Accordingly use of mobile devices, such as

    cell phones and personal digital assistants (PDAs), is diversi-

    fied. However, PINs are still adopted as the only security

    mechanism for those mobile devices. Because of their limited

    Table 7 Comparing the performance with related works.

    Input string Feature

    Clarke and Furnell

    (2005, 2007a,b)

    4-Digit PIN Inter-keystroke latency

    11-Digit number Inter-keystroke latency

    6-Digit text msg. Inter-keystroke latency

    Hwang et al. (2007) 4-Digit PINs Duration and interval

    4-Digit PINs Duration and intervalonly as a reference. In practice, depending on applications,

    FAR may be more important than FRR or vice versa. The issue

    could be addressed by proper threshold selection.

    Acknowledgement

    This work was supported by grant no. R01-2005-000-103900-

    0 from Basic Research Program of the Korea Science and

    Engineering Foundation, the Brain Korea 21 program in 2006

    and partially supported by Engineering Research Institute of

    SNU.r e f e r e n c e s

    Chen GD, Chang CK, Wang CY. Ubiquitous learning website:scaffold learners by mobile devices with information-awaretechniques. Computers & Education 2008;50(1):7790.

    Cho S, Hwang S. Artificial rhythms and cues for keystrokedynamics-based authentication. Lecture Notes in ComputerScience (LNCS) 2006;3832:62632.

    Clarke N, Furnell S. Authentication of users on mobile telephones a survey of attitudes and practices. Computers & Security2005;24(7):51927.

    Clarke N, Furnell S. Advanced user authentication for mobiledevices. Computers & Security 2007a;26(2):10919.

    Clarke N, Furnell S. Authenticating mobile phone users usingkeystroke analysis. International Journal of InformationSecurity 2007b;6(1):114.

    Fawcett T. An introduction to ROC analysis. Pattern RecognitionLetters 2006;27(8):86174.

    Gaines R, Lisowski W, Press S, Shapiro N. Authentication bykeystroke timing: some preliminary results. Rand ReportR-256-NSF. Rand Corporation; 1980.

    Golarelli M, Maio D, Maltoni D. On the error reject trade-off inbiometric verification systems. IEEE Transactions on PatternAnalysis and Machine Intelligence 1997;19(7):78696.

    Hwang S, Cho S, Park S. Mobile User authentication usingkeystroke dynamics analysis. In: Proceedings of the KoreanOperations Research and Management Science Society(KORMS) conference, Seoul, Korea, 17 November, 2007; 2007a,p. 652655.

    Hwang S, Lee H, Cho S. Improving authentication accuracy usingartificial rhythms and cues for keystroke dynamics-basedauthentication, submitted for publication.

    International Biometric Group. How is biometrics defined? http://www.biometricgroup.com/reports/public/reports/biometric_definition.html.

    Kowalski S, Goldstein M. Consumers awareness of, attitudestowards and adoption of mobile phone security. In: 20thinternational symposium on human factors intelecommunication, Sophia-Antipolis, France, 2023 March2006.

    Qualcomm. CDMA2000 1xEV-DO overview. Available from: http://

    Artificial Rhythmswith Cues

    No. of patterns fortraining (or validation)

    EER (%)

    No 30 916

    No 30 513

    No 30 1521

    No 5 13

    Yes 5 4www.cdmatech.com/download_library/pdf/QCOM_1xEV-DO.pdf.

    SAMSUNG Electronics website. http://www.samsung.com.Umphress D, Williams G. Identity verification through keyboard

    characteristics. International Journal of Man Machine Studies1985;23:26373.

    WIPI website. http://www.wipi.or.kr/English/index.html.

    Seong-seob Hwang is currently a PhD candidate in the

    Department of Industrial Engineering, Seoul National

    University, Korea. Before entering graduate school, He worked

    as a system engineer at SAMSUNG SDS. His research interests

  • include data mining, pattern recognition, and their

    applications.

    Sungzoon Cho is a professor in the Department of Industrial

    Engineering, College of Engineering, Seoul National Univer-

    sity, Korea. His research interests are neural network, pattern

    recognition, data mining, and their applications in various

    areas such as response modeling and keystroke-based

    authentication. He published over 100 papers in various

    journals and proceedings. He also holds a US patent and

    a Korean patent concerned with keystroke-based user

    authentication.

    Sunghoon Park received BS of Computer Science in 2005, and

    is currently a PhD candidate in the Department of Industrial

    Engineering, College of Engineering, Seoul National Univer-

    sity, Korea. His research interests include financial engi-

    neering and marketing applications.

    c om p u t e r s & s e c u r i t y 2 8 ( 2 0 0 9 ) 8 5 9 3 93

    Keystroke dynamics-based authentication for mobile devicesIntroductionKeystroke dynamics-based authentication for mobile devicesKeystroke dynamics-based authentication (KDA)Improving data qualityMobile application

    Performance evaluationData collectionExperimental results

    Discussion and conclusionsAcknowledgementReferences

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