User Authentication Using Keystroke Dynamics via Crowdsourcing

Foresi, Andrew; Samavi, Reza

doi:10.1109/pst47121.2019.8949031

Cited by 8 publications

(3 citation statements)

References 12 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%

“…In some few cases, the subject size is large. A few studies [155], [179], [211] included 250 subjects where a study [212] considered 300 subjects. A study [213] included 283 subjects, while some of the few studies [102], [214]- [217] considered more than 300 subjects.…”

Section: F Subject Size and Subject Selectionmentioning

confidence: 99%

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

et al. 2022

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“…User templates are created by calculating the mean and standard deviation of each key hold time and key digraph flight and latency [12]. On the other hand, some research studies [6] had featured the mean and standard deviation of only those digraphs which had occurred least number of times in order to build the inimitable feature set.…”

Section: Literature Reviewmentioning

confidence: 99%

Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

et al. 2020

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User authentication is considered to be an important aspect of any cyber security program. However, one-time validation of user's identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented which tends to authenticate the user on each action. The proposed 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model, decides the genuineness of user based on each action and locks out the imposter user based on final threshold, however, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II.

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Toward an Adversarial Model for Keystroke Authentication in Embedded Devices

Hegde¹,

Sankaran²

2021

Lecture Notes in Electrical Engineering

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User Authentication Using Keystroke Dynamics via Crowdsourcing

Cited by 8 publications

References 12 publications

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

Toward an Adversarial Model for Keystroke Authentication in Embedded Devices

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