A multiple layer fusion approach on keystroke dynamics

Teh, Pin Shen; Teoh, Andrew Beng Jin; Connie, Tee; Ong, Thian Song

doi:10.1007/s10044-009-0167-9

Cited by 19 publications

(14 citation statements)

References 4 publications

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“…Their technique involved key hold times and latency, using a Gaussian mixture model and a neural network. This, and many other similar studies [ 16 , 17 , 18 , 19 ], demonstrate that keystroke characteristics can be used very accurately to classify the features of particular users.…”

Section: Introductionsupporting

confidence: 64%

High-accuracy detection of early Parkinson's Disease using multiple characteristics of finger movement while typing

Adams

2017

PLoS ONE

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Parkinson’s Disease (PD) is a progressive neurodegenerative movement disease affecting over 6 million people worldwide. Loss of dopamine-producing neurons results in a range of both motor and non-motor symptoms, however there is currently no definitive test for PD by non-specialist clinicians, especially in the early disease stages where the symptoms may be subtle and poorly characterised. This results in a high misdiagnosis rate (up to 25% by non-specialists) and people can have the disease for many years before diagnosis. There is a need for a more accurate, objective means of early detection, ideally one which can be used by individuals in their home setting. In this investigation, keystroke timing information from 103 subjects (comprising 32 with mild PD severity and the remainder non-PD controls) was captured as they typed on a computer keyboard over an extended period and showed that PD affects various characteristics of hand and finger movement and that these can be detected. A novel methodology was used to classify the subjects’ disease status, by utilising a combination of many keystroke features which were analysed by an ensemble of machine learning classification models. When applied to two separate participant groups, this approach was able to successfully discriminate between early-PD subjects and controls with 96% sensitivity, 97% specificity and an AUC of 0.98. The technique does not require any specialised equipment or medical supervision, and does not rely on the experience and skill of the practitioner. Regarding more general application, it currently does not incorporate a second cardinal disease symptom, so may not differentiate PD from similar movement-related disorders.

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Section: Introductionsupporting

confidence: 64%

High-accuracy detection of early Parkinson's Disease using multiple characteristics of finger movement while typing

Adams

2017

PLoS ONE

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“…Another motivation for using signals H and DD comes from experimental results presented by Teh et al [23], where H yielded the best result among all keystroke latencies individually tested, whereas H and DD yielded the best combined result. In that study, a database with 100 users typing "the brown fox" was used.…”

Section: Interval Description and Signal Preprocessingmentioning

confidence: 97%

Contributions to empirical analysis of keystroke dynamics in passwords

Montalvão

Freire

Bezerra

et al. 2015

Pattern Recognition Letters

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“…More details of these works are given in [9] and [10]. Recently in the mobile context, potential results have been achieved [10][11][12][13][14]. However, the inherent limitations of single behavior modalities make them difficult to achieve good result enough to implement in real world.…”

Section: Related Workmentioning

confidence: 99%

“…On keystroke dynamics, P.S. Teh [11] studied on multilayer fusion of keystroke dynamics (username, password, 13-character text) on 100 subjects using digraph features and achieved equal error rate (EER) at 1.401% when using template matching with Gaussian Probability Distribution Function and Direction Similarity Measure and score fusion. In 2013, M. Trojath [12] studied on 11-character password on 18 subjects with many features such as digraph, trigraph, pressure, etc.…”

Section: Related Workmentioning

confidence: 99%

“…on smartphone. Nevertheless, because of the inherent limitations [7,8] such as noisy data, high intra-class variability, and high inter-class similarity, existing work on authentication using single behavioral biometric [9][10][11][12][13][14] did not achieve performance which is good enough to implement in real world. Thus, multimodal biometrics has been proposed to alleviate these limitations [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Using Keystroke Dynamics for Implicit Authentication on Smartphone

Do¹,

Hoang²,

Luong³

et al. 2014

Journal of Korea Multimedia Society

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Authentication methods on smartphone are demanded to be implicit to users with minimum users' interaction. Existing authentication methods (e.g. PINs, passwords, visual patterns, etc.) are not effectively considering remembrance and privacy issues. Behavioral biometrics such as keystroke dynamics and gait biometrics can be acquired easily and implicitly by using integrated sensors on smartphone. We propose a biometric model involving keystroke dynamics for implicit authentication on smartphone. We first design a feature extraction method for keystroke dynamics. And then, we build a fusion model of keystroke dynamics and gait to improve the authentication performance of single behavioral biometric on smartphone. We operate the fusion at both feature extraction level and matching score level. Experiment using linear Support Vector Machines (SVM) classifier reveals that the best results are achieved with score fusion: a recognition rate approximately 97.86% under identification mode and an error rate approximately 1.11% under authentication mode.

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A multiple layer fusion approach on keystroke dynamics

Cited by 19 publications

References 4 publications

High-accuracy detection of early Parkinson's Disease using multiple characteristics of finger movement while typing

High-accuracy detection of early Parkinson's Disease using multiple characteristics of finger movement while typing

Contributions to empirical analysis of keystroke dynamics in passwords

Using Keystroke Dynamics for Implicit Authentication on Smartphone

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