Keystroke Dynamics Authentication Using Small Datasets

Raul, Nataasha; Dmello, Royston; Bhalerao, Mandar

doi:10.1007/978-981-13-7561-3_7

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Year Env. Type Special arrangement Objective [146] 2017 D F HTML and Javascript To collect KD data through web-page [147] 2017 D F Implemented in python micro framework flask Web-based application to collect KD data [148] 2017 O C Data sampling rate at 100Hz For down sampling to 50Hz, 30Hz, 10Hz, 3Hz as per demand [149] 2018 D C VB .NET for windows form application To collect KD data for frequent English terms [139] 2018 D F HTML, CSS, and JavaScript To collect typing style while transcribe 15 English sentences [150] 2018 O F Triboelectric Nanogenerator For developing intelligent keyboard [151] 2019 D C HTML, JavaScript and MySQL To collect KD data from students through online courses [152] 2019 D F JavaScript To collect KD data for web-based password driven systems [153] 2019 D F Django web app To collect KD data [154] 2019 D F Kotlin language, JavaFX To collect KD data with sound [155] 2019 D F HTML and JavaScript To collect KD data via crowdsourcing [156] 2019 D C Application developed by VB C# To collect KD data continuously [157] 2019 D C "Pynput" keyboard event listener library To collect KD data [158] 2019 A few latest studies used TOSHIBA Dynabook RZ82/T [189], MacBook Pro [206], ASUS K56C [207] for laptop security. A study [208] used Emotiv EPOC to measure cognitive load in addition to KD features while typing on a device with limited sensors like a conventional keyboard.…”

Section: Studymentioning

confidence: 99%

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A Systematic Literature Review on Latest Keystroke Dynamics Based Models

et al. 2022

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The purpose of this study is to conduct a comprehensive evaluation and analysis of the most recent studies on the implications of keystroke dynamics (KD) patterns in user authentication, identification, and the determination of useful information. Another aim is to provide an extensive and up-to-date survey of the recent literature and potential research directions to understand the present state-of-the-art methodologies in this particular domain that are expected to be beneficial for the KD research community. From January 1st, 2017 to March 13th, 2022, the popular six electronic databases have been searched using a search criterion ("keystroke dynamics" OR "typing pattern") AND ("authentication" OR "verification" OR "identification"). With this criterion, a total of nine thousand three hundred forty-eight results, including duplicates, were produced. However, one thousand five hundred forty-seven articles have been chosen after removing duplicates and preliminary screening. Due to insufficient information, only one hundred twentyseven high-quality quantitative research articles have been included in the article selection process. We compared and summarised several factors with multiple tables to comprehend the various methodologies, experimental settings, and findings. In this study, we have identified six unique KD-based designs and presented the status of findings toward an effective solution in authentication, identification, and prediction. We have also discovered considerable heterogeneity across studies in each KD-based design for desktops and smartphones separately. Finally, this paper found a few open research challenges and provided some indications for a deeper understanding of the issues and further study.

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Section: Studymentioning

confidence: 99%

“…A study [324] used both EER and accuracy to measure the performance of a distance-based detector. A few studies [153], [325], [326] used FAR and FRR for the model performance. These metrics are common in the user authentication model, which is widely accepted in industry and academia [327].…”

Section: ) Performance Evaluation Metrics For Data Acquisitionmentioning

confidence: 99%