Keystroke Dynamics Overview

Giot, Romain; El-Abed, Mohamad; Rosenberger, Christophe

doi:10.5772/17064

Cited by 31 publications

(25 citation statements)

References 72 publications

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“…Password 1 in combination with the age soft biometric and Password 3 with the gender soft biometric), the EER value only slightly increases compared to the baseline performance. Next, we tested the combination of the three soft criteria with the distance score by Equation (4) and found that the results are of the same order i.e. between 14.88% and 19.05%.…”

Section: Fusion Processmentioning

confidence: 99%

“…In terms of keystroke dynamics, two events have taken place and both events are stored as raw data. In both cases, three values are stored, being the value of the key, the type of event (either press or release) and the time the event took place [4]. From this, one can calculate features used in keystroke dynamics.…”

Section: Data Description and Extractionmentioning

confidence: 99%

“…We then applied the same analysis, but, only using Equations (3) and (5) to find the verification score v i instead of Equations (2) and (4). Experimental results can be found in Table 4.…”

Section: Fusion Processmentioning

confidence: 99%

“…It is a behavioral biometric modality as well as signature dynamics, gait, voice [1,2,3]. Among its advantages in comparison to other modalities, we can mention that it is a low-cost with no extra sensor or device is required [4,5]. The counterpart to this benefits is the worse performances compared to those morphological biometric modalities (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Keystroke dynamics performance enhancement with soft biometrics

Idrus

Cherrier

Rosenberger

et al. 2015

IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)

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It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural biometric modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft biometrics. Soft biometrics refers to biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.

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Section: Fusion Processmentioning

confidence: 99%

Section: Data Description and Extractionmentioning

confidence: 99%

“…We then applied the same analysis, but, only using Equations (3) and (5) to find the verification score v i instead of Equations (2) and (4). Experimental results can be found in Table 4.…”

Section: Fusion Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Keystroke dynamics performance enhancement with soft biometrics

Idrus

Cherrier

Rosenberger

et al. 2015

IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)

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“…On the other hand, biometrics [51,52,54,74,87,104,106,109] based on "who" the person is or "how" the person behaves present a significant security advancement to meet these new challenges. Among them, keystroke dynamics [3,11,38,78,83,84,85,96,99,105] provide a natural choice for secure "password-free" computer access. Keystroke dynamics refer to the habitual patterns or rhythms an individual exhibits while typing on a keyboard input device.…”

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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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Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics

Earl

Campbell

Buckley

2021

Lecture Notes in Networks and Systems

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In this preliminary paper, we investigate the use of keystroke and mouse dynamics as a means of identifying soft biometric features. We present evidence that combining features from both provides a more accurate means of identifying all of the soft biometric traits investigated regardless of the machine learning method used. The data presented in this paper gives a thorough breakdown of accuracy scores from multiple machine learning methods and numbers of features used.

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Keystroke Dynamics Overview

Cited by 31 publications

References 72 publications

Keystroke dynamics performance enhancement with soft biometrics

Keystroke dynamics performance enhancement with soft biometrics

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

Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics

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