On the equalization of keystroke timing histograms

Montalvão, Jugurta; Freire, Eduardo Oliveira

doi:10.1016/j.patrec.2006.01.010

Cited by 56 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In 2006, a method was published proposing the use of timeinterval equalization as a nonlinear memoryless preprocessing approach to improve performances of most keystroke-based biometric methods [15]. Databases of static and free text were acquired to show, in terms of error rates, the resulting gains that equalization could provide to most known methods published to that date.…”

Section: Interval Description and Signal Preprocessingmentioning

confidence: 99%

“…These experiments, whose results are shown in Table 1 and throughout this subsection, study the preprocessing gains in terms of Equal Error Rate (EER, the operational point of a biometric detector for which False Rejection Rate equals False Acceptance Rate). However, we emphasize that the preprocessing step is just an auxiliary tool in this work, whose details are to be found in [15].…”

Section: Interval Description and Signal Preprocessingmentioning

confidence: 99%

“…In Section 3, the preprocessing step (or time interval equalization) first proposed in 2006 [15] is briefly recalled and adjusted to process both down-down keystroke latency and key hold time. Improvements from this preprocessing step are shown through experiments adapted from papers by the owners of the databases presented in Section 2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Contributions to empirical analysis of keystroke dynamics in passwords

Montalvão

Freire

Bezerra

et al. 2015

Pattern Recognition Letters

Self Cite

View full text Add to dashboard Cite

Section: Interval Description and Signal Preprocessingmentioning

confidence: 99%

Section: Interval Description and Signal Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Contributions to empirical analysis of keystroke dynamics in passwords

Montalvão

Freire

Bezerra

et al. 2015

Pattern Recognition Letters

Self Cite

View full text Add to dashboard Cite

“…The static entry datasets are publicly available in Killorhy and Maxion [3], Giot et al [4], Loy et al [1] [2]. The free text databases are:Biochaves by Filho and Freire [5] and Clarksons University Keystroke Dataset by Vural et al [6].…”

Section: Literature Surveymentioning

confidence: 99%

Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier

Bhatia¹,

Hanmandlu²

2018

JMP

View full text Add to dashboard Cite

This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.

show abstract

“…Some researchers have made their datasets available to the public, including works of Allen, Bello, Giot, Jugurta, and Maxion [11,[13][14][15][16]. Most of the existing datasets are based on "static text, " with only one exception of the BioChaves dataset, which contains both static and free text [16]. Compared with most mature biometrics, keystroke dynamics is still at its very early stage, and various existing public keystroke datasets containing subject only range from over a dozen to over one hundred.…”

Section: The Literature Surveymentioning

confidence: 99%

Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets

Deng¹,

Zhong²

2013

ISRN Signal Processing

View full text Add to dashboard Cite

User authentication using keystroke dynamics offers many advances in the domain of cyber security, including no extra hardware cost, continuous monitoring, and nonintrusiveness. Many algorithms have been proposed in the literature. Here, we introduce two new algorithms to the domain: the Gaussian mixture model with the universal background model (GMM-UBM) and the deep belief nets (DBN). Unlike most existing approaches, which only use genuine users' data at training time, these two generative model-based approaches leverage data from background users to enhance the model's discriminative capability without seeing the imposter's data at training time. These two new algorithms make no assumption about the underlying probability distribution and are fast for training and testing. They can also be extended to free text use cases. Evaluations on the CMU keystroke dynamics benchmark dataset show over 58% reduction in the equal error rate over the best published approaches.

show abstract

On the equalization of keystroke timing histograms

Cited by 56 publications

References 6 publications

Contributions to empirical analysis of keystroke dynamics in passwords

Contributions to empirical analysis of keystroke dynamics in passwords

Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier

Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets

Contact Info

Product

Resources

About