Statistical Fusion Approach on Keystroke Dynamics

Teh, Pin Shen; Teoh, Andrew Beng Jin; Ong, Thian Song; Neo, Han Foon

doi:10.1109/sitis.2007.46

Cited by 29 publications

(11 citation statements)

References 7 publications

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“…Furthermore, a research that has considered fusion in keystroke dynamics is that conducted by Teh et al [12]. The authors of this research proposed a fusion between two methods.…”

Section: Keystroke Dynamicsmentioning

confidence: 99%

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Improving the performance of free-text keystroke dynamics authentication by fusion

Alsultan

Warwick

Wei

2018

Applied Soft Computing

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Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00% False Accept Rate (FAR) and 0.00% False Reject Rate (FRR).

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“…Furthermore, a research that has considered fusion in keystroke dynamics is that conducted by Teh et al [12]. The authors of this research proposed a fusion between two methods.…”

Section: Keystroke Dynamicsmentioning

confidence: 99%

“…Once the key-pairs have been obtained from the users' raw data, the keystroke features are extracted [12]. These features were computed for every key and key-pair using two main values, specifically: the press time (Dn) and the release time (Un) of each key (n) in milliseconds.…”

Section: Timing Featuresmentioning

confidence: 99%

Improving the performance of free-text keystroke dynamics authentication by fusion

Alsultan

Warwick

Wei

2018

Applied Soft Computing

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“…• Different kinds of weighted sums score fusion functions are proposed in Giot, El-Abed & Rosenberger (2010); Teh et al (2007).…”

Section: Improving the Performancementioning

confidence: 99%

Keystroke Dynamics Overview

Giot¹,

El-Abed²,

Rosenberger³

2011

Biometrics

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“…Numerous studies in keystroke dynamics have been proposed utilizing the statistical models to build the classifier [3]- [7], machine learning approach [8]- [12] and hybrid models [3], [13]- [15]. However, the model accuracy to differentiate typing pattern between genuine user and complexity of accessing multitude data are the significant challenges in those models [16].…”

Section: Introductionmentioning

confidence: 99%

Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron

Andrean¹,

Jayabalan²,

Thiruchelvam³

2020

IJMLC

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User authentication is an essential factor to protect digital service and prevent malicious users from gaining access to the system. As Single Factor Authentication (SFA) is less secure, organizations started to utilize Multi-Factor Authentication (MFA) to provide reliable protection by using two or more identification measures. Keystroke dynamics is a behavioral biometric, which analyses users typing rhythm to identify the legitimacy of the subject accessing the system. Keystroke dynamics that have a low implementation cost and does not require additional hardware in the authentication process since the collection of typing data is relatively simple as it does not require extra effort from the user. This study aims to propose deep learning model using Multilayer Perceptron (MLP) in keystroke dynamics for user authentication on CMU benchmark dataset. The user typing rhythm from 51 subjects collected based on the static password (.tie5Roanl) typed 400 times over 8 sessions and 50 repetitions per session. The MLP achieved optimum EER of 4.45% compared to original benchmark classifiers such as 9.6% (scaled Manhattan), 9.96% (Mahalanobis Nearest Neighbor), 10.22% (Outlier Count), 10.25% and 16.14% (Neural Network Auto-Assoc).

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Statistical Fusion Approach on Keystroke Dynamics

Cited by 29 publications

References 7 publications

Improving the performance of free-text keystroke dynamics authentication by fusion

Improving the performance of free-text keystroke dynamics authentication by fusion

Keystroke Dynamics Overview

Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron

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