Dynamic keystroke analysis using AR model

Eltahir, W.E.; Salami, Momoh Jimoh Eyiomika; Ismail, Ahmad Faris; Lai, Weng Kin

doi:10.1109/icit.2004.1490798

Cited by 13 publications

(14 citation statements)

References 2 publications

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“…As for the keystroke dynamics, they refer to the typing habits that cannot be easily imitated by other people; in consequence, the KDA system can utilize the time differences of keystroke time to judge and verify whether the user is a legitimate user or impostor. In the past relevant studies with regard to keystroke authentication, the researchers collect diverse keystroke time of typing fixedtext usernames or passwords as the characteristics [13,14] and then distinguish legitimate users from impostors by means of K-nearest-neighbor (KNN), statistical classifier [5], neural networks [15], sorting [16], and fuzzy inference [17] methods.…”

Section: Introductionmentioning

confidence: 99%

“…PR denotes the time period between the press and the release of the same key on a keyboard; PP denotes the time period between the press of one key and the press of another key; RP denotes the time period between the release of one key and the press of another key; and RR denotes the time period between the release of one key and the release of another key. As for the past studies regarding keystroke authentication, most of them took the four time periods PR, RP, RR, and PP produced when the user entered the fixed-text usernames and passwords as the resources of the authentication [5,6,13,22] Moreover, in the authentication phase, lots of works proposed a variety of methods to distinguish legitimate users from impostors by means of KNN, statistical [5], neural networks [15], sorting [16], and fuzzy inference [17] methods. According to the research results reported by Boechat et al [23], they mentioned that the statistical classifier has the following characteristics such as simplification, speed, low-burden calculation, and the accuracy of identification as others.…”

Section: Introductionmentioning

confidence: 99%

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A changeable personal identification number‐based keystroke dynamics authentication system on smart phones

Chang

Tsai

et al. 2015

Security Comm Networks

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One can apply cell phones to access e-bank, buy stocks, pay credit card bills, and so on. The security issues of cell phones become extremely important. Most of subscribers use personal identification number (PIN) codes which combined with 6-8 numbers to protect their subscriber identity module cards from illegal accesses. It is easily to be decoded by the dictionary attack or shoulder surfing attack. Many studies employed keystroke dynamics to protect the PIN code, and the relative results exhibit that keystroke dynamics can indeed improve the security of a PIN code. However, the traditional keystroke dynamics-based authentication (KDA) system has to collect user's keystroke dynamics firstly and then produce a unique personal biometrics. It is inconvenient for users when changing their PIN codes is required, because the corresponding KDA systems should be retrained. To solve the previously mentioned drawbacks, this paper proposes a novel technique that allows users to change their PIN codes anytime without any extra retraining. This technique not only enhance the security of the PIN codes but also enrich the security of accessing e-bank, buying stocks, paying credit card bill, and other service via smart phones. Conducted experiment results show that the proposed system can effectively improve the KDA system to distinguish legitimate users and impostors even when users change their original passwords. Copyright

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A changeable personal identification number‐based keystroke dynamics authentication system on smart phones

Chang

Tsai

et al. 2015

Security Comm Networks

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“…The algorithms span a vast range of families: basic statistical features789, Bayesian analysis1011, autoregressive models12, hidden Markov models1314, artificial neural networks1516 and other machine learning techniques1718. Each of these approaches is employed to grant or deny access to a computer system, hence, a primary requirement is the reliance on a small number of key presses in order to avoid excessive burden on the user who needs to access the system.…”

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confidence: 99%

Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Giancardo

Sánchez‐Ferro

Butterworth

et al. 2015

Sci Rep

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Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population.

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“…Auto-regressive (AR) and AR moving-average (ARMA) models were considered with and without measures of pressure (Changshui and Yanhua, 2000; Eltahir et al, 2004). Hidden Markov models (HMMs) have been implemented (Chang, 2005) with a similarity histogram, and, by attempting to recognize patterns, produce promising results.…”

Section: Keystroke Dynamics For Securitymentioning

confidence: 99%

“…The most applicable and investigated addition was that of keystroke pressure. Measures of pressure were achieved by making use of piezo-electric and piezo-resistive sensors interfaced with the computer system to which the active keyboard was connected (Eltahir et al, 2003, 2004; Nonaka and Kurihara, 2004). These sensors were either placed beneath specific (or all) keys (Eltahir et al, 2003, 2004) or upon the support sections of the keyboard (Nonaka and Kurihara, 2004).…”

Section: Keystroke Dynamics For Securitymentioning

confidence: 99%

Keystroke dynamics in the pre-touchscreen era

Ahmad

Szymkowiak

Campbell

2013

Front. Hum. Neurosci.

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Biometric authentication seeks to measure an individual’s unique physiological attributes for the purpose of identity verification. Conventionally, this task has been realized via analyses of fingerprints or signature iris patterns. However, whilst such methods effectively offer a superior security protocol compared with password-based approaches for example, their substantial infrastructure costs, and intrusive nature, make them undesirable and indeed impractical for many scenarios. An alternative approach seeks to develop similarly robust screening protocols through analysis of typing patterns, formally known as keystroke dynamics. Here, keystroke analysis methodologies can utilize multiple variables, and a range of mathematical techniques, in order to extract individuals’ typing signatures. Such variables may include measurement of the period between key presses, and/or releases, or even key-strike pressures. Statistical methods, neural networks, and fuzzy logic have often formed the basis for quantitative analysis on the data gathered, typically from conventional computer keyboards. Extension to more recent technologies such as numerical keypads and touch-screen devices is in its infancy, but obviously important as such devices grow in popularity. Here, we review the state of knowledge pertaining to authentication via conventional keyboards with a view toward indicating how this platform of knowledge can be exploited and extended into the newly emergent type-based technological contexts.

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Dynamic keystroke analysis using AR model

Cited by 13 publications

References 2 publications

A changeable personal identification number‐based keystroke dynamics authentication system on smart phones

A changeable personal identification number‐based keystroke dynamics authentication system on smart phones

Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Keystroke dynamics in the pre-touchscreen era

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