Are Digraphs Good for Free-Text Keystroke Dynamics?

Sim, Terence; Janakiraman, Rajkumar

doi:10.1109/cvpr.2007.383393

Cited by 64 publications

(46 citation statements)

References 6 publications

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“…One of the most popular features used to model keystroke dynamics is digraph latency [10,9,3], which calculates the time difference between pressing the keys of two adjacent letters. It has been shown that the word-specific digraph is much more discriminative than the generic digraph, which is computed without regard to what letters were typed [15]. Considering that the acoustic signal from keystroke does not explicitly carry the information of what letter is typed, we propose a novel approach to employ the digraph feature by constructing a virtual alphabet.…”

Section: Keystroke Sound Biometricmentioning

confidence: 99%

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Biometric authentication via keystroke sound

Liu

Ross

Metaxas

2013

2013 International Conference on Biometrics (ICB)

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Unlike conventional one shot biometric authentication schemes, continuous authentication has a number of advantages, such as longer time for sensing, ability to rectify authentication decisions, and persistent verification of a user's identity, which are critical in applications demanding enhanced security. However, conventional modalities, e.g., face, fingerprint, and keystroke dynamics, have various drawbacks in continuous authentication scenarios. In light of this, this paper proposes a novel non-intrusive and privacy-friendly biometric modality that utilizes keystroke sound. Given the keystroke sound recorded by a lowcost microphone, our system extracts discriminative features and performs matching between a gallery stream and a probe stream. Motivated by the concept of digraphs used in modeling keystroke dynamics, we learn a virtual alphabet from keystroke segments, from which the digraph latency within pairs of virtual letters, as well as other statistical features, are utilized by the score functions. The resultant multiple scores are indicative of the similarities between two sound streams, and are fused to make a final authentication decision. We collect a first-of-its-kind keystroke sound database of 45 subjects typing on a keyboard. Experiments on static text-based authentication, demonstrate the potential as well as limitations of this novel biometric modality.

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Section: Keystroke Sound Biometricmentioning

confidence: 99%

“…It has also been shown that word-specific digraph, which is computed on the same two keys, is much more discriminative than the generic digraph [15]. In keystroke dynamics, such word information is readily available since key logging records the letter associated with each keystroke.…”

Section: Virtual Alphabet Via Clusteringmentioning

confidence: 99%

Biometric authentication via keystroke sound

Liu

Ross

Metaxas

2013

2013 International Conference on Biometrics (ICB)

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“…Trigraphs, which are the time latencies between every three consecutive keys, and similarly, n-graphs, have been investigated as well. In their study on keystroke analysis using free text, Sim and Janakiraman [27] investigated the effectiveness of digraphs and more generally n-graphs for free text keystroke biometrics, and concluded that ngraphs are discriminative only when they are wordspecific. As such, the digraph and n-graph features do depend on the word context they are computed in.…”

Section: Introductionmentioning

confidence: 99%

Keystroke dynamics for user authentication

Zhong

Deng

Jain

2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

117

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In this paper we investigate the problem of user authentication using keystroke biometrics. A new distance metric that is effective in dealing with the challenges intrinsic to keystroke dynamics data, i.e., scale variations, feature interactions and redundancies, and outliers is proposed. Our keystroke biometrics algorithms based on this new distance metric are evaluated on the CMU keystroke dynamics benchmark dataset and are shown to be superior to algorithms using traditional distance metrics. IntroductionWith the ever increasing demand for more secure access control in many of today's security applications, traditional methods such as PINs, tokens, or passwords fail to keep up with the challenges presented because they can be lost or stolen, which compromises the system security. [26] provides a natural choice for secure "password-free" computer access. Keystroke dynamics refers to the habitual patterns or rhythms an individual exhibits while typing on a keyboard input device. These rhythms and patterns of tapping are idiosyncratic [5], in the same way as handwritings or signatures, due to their similar governing neurophysiological mechanisms. As early as in the 19 th century, telegraph operators could recognize each other based on one's specific tapping style [18]. This suggests that keystroke dynamics contain sufficient information to serve as a potential biometric identifier to ascertain a specific keyboard user.Compared to other biometrics, keystroke biometrics has additional desirable properties due to its user-friendliness and non-intrusiveness. Keystroke dynamics data can be collected without a user's cooperation or even awareness.Continuous authentication is possible using keystroke dynamics just as a mere consequence of people's use of computers. Unlike many other biometrics, the temporal information of keystrokes can be collected to ascertain a user using only software and no additional hardware. In summary, keystroke dynamics biometrics enables a cost effective, user friendly, and continuous user authentication with potential for high accuracy.Although keystroke dynamics is governed by a person's neurophysiological pathway to be highly individualistic, it can also be influenced by his or her psychological state. As a "behavioral" biometrics [35], keystroke dynamics exhibits instabilities due to transient factors such as emotions, stress, and drowsiness etc [6]. It also depends on external factors, such as the input keyboard device used, possibly due to different layout of the keys. The keying times can be noisy with outliers. As keystroke biometrics exploits the habitual rhythm in typing, it has been observed that keystrokes of frequently typed words or strings show more consistency and are better discerners [22][38].Keystroke biometrics can use "static text", where keystroke dynamics of a specific pre-enrolled text, such as a password, is analyzed at a certain time, e.g., during the log on process. For more secure applications, "free text" should be used to continuously authenticate a user...

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“…Trigraphs, which are the time latencies between every three consecutive keys, and similarly, ngraphs, have been investigated as well. In their study on keystroke analysis using free text, Sim and Janakiraman [94] investigated the effectiveness of digraphs and more generally n-graphs for free text keystroke biometrics, and concluded that n-graphs are discriminative only when they are word-specific. As such, the digraph and n-graph features do depend on the word context they are computed in.…”

Section: Keystroke Dynamics Featuresmentioning

confidence: 99%

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

Zhong¹,

Deng²

2015

Gate to Computer Science and Research

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In this review paper we present a comprehensive survey of research efforts in the past couple of decades on keystroke dynamics biometrics. We review the literature in light of various feature extraction, feature matching and classification methods for keystroke dynamics. We also discuss recent trends in keystroke dynamics research, including its use in mobile environments, as a soft biometrics, and its fusion with other biometric modalities. We further address the evaluation of keystroke biometric systems, including traditional and new performance metrics, and list publicly available keystroke datasets for performance benchmarks to promote synergy in the research community.

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Are Digraphs Good for Free-Text Keystroke Dynamics?

Cited by 64 publications

References 6 publications

Biometric authentication via keystroke sound

Biometric authentication via keystroke sound

Keystroke dynamics for user authentication

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

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