Factors affecting keystroke dynamics for verification data collecting and analysis

Zaidan, Dema; Salem, Asma; Swidan, Andraws; Saifan, Ramzi

doi:10.1109/icitech.2017.8080032

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…Vasyl, Sharapova, Ivanova, Denis, and Yulia [51] developed a web-based authentication system based on users' keystroke features and suggested merging KD with other human features to achieve greater precision in authenticating users. Zaidan, Salem, Swidan, and Saifan [27] developed a web-based application for use in the study of the factors affecting KD in mobile systems that extracts and stores features such as the characters typed, keyhold latency, up-down latency, down-down latency, and overall latency; they then tested factors such as the device used for typing, the knowledge of the text, the mood of the user, and the complexity of the password on this dataset and achieved positive results. Boakye Osei, Opanin Gyamfi, and Okoe Alhassan [52] proposed a web-based keystroke login system using features such as dwell, flight, and locate to minimize error rates.…”

Section: E Web-based Behavioral Authenticationmentioning

confidence: 99%

“…However, this can be enhanced through multifactor authentication systems that combine passwords, biometrics, and OTP verification [26]. The study [27] observed that, to control access to data, most systems rely on usernames and passwords for authentication, which are both convenient and insecure because they can be quickly entered into an online application or service [28]. Since the human capacity for information processing is limited, users face difficulties in remembering and matching their passwords and, therefore, often use either easy-to-guess passwords or complex passwords that are hard to remember [29].…”

Section: A Challenges In Password Authenticationmentioning

confidence: 99%

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A Secure and Scalable Behavioral Dynamics Authentication Model

Oladipo,

Awotunde,

Nicho

et al. 2023

IJACSA

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Various authentication methods have been proposed to mitigate data breaches. However, the increasing frequency of data breaches and users' lack of awareness have exposed traditional methods, including single-factor passwordbased systems and, two-factor authentication systems, to vulnerabilities against attacks. While behavioral authentication holds promise in tackling these issues, it faces challenges concerning interoperability between operating systems, the security of behavioral data, accuracy enhancement, scalability, and cost. This research presents a scalable dynamic behavioral authentication model utilizing keystroke typing patterns. The model is constructed around five key components: humancomputer interface devices, encryption of behavioral data, consideration of the authenticator's emotional state, incorporation of cross-platform features, and proposed implementation solutions. It addresses potential typing errors and employs data encryption for behavioral data, achieving a harmonious blend of usability and security by leveraging keyboard dynamics. This is accomplished through the implementation of a web-based authentication system that integrates Convolutional Neural Networks (CNNs) for advanced feature engineering. Keystroke typing patterns were gathered from participants and subsequently employed to evaluate the system's keystroke timing verification, login ID verification, and error handling capabilities. The web-based system uniquely identifies users by merging their username-password (UN-PW) credentials with their keyboard typing patterns, all while securely storing the keystroke data. Given the achievement of a 100% accuracy rate, the proposed Behavioral Dynamics Authentication Model (BDA) introduces future researchers to five scalable constructs. These constructs offer an optimal combination, tailored to the device and context, for maximizing effectiveness. This achievement underscores its potential applications in the realm of authentication.

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Section: E Web-based Behavioral Authenticationmentioning

confidence: 99%

Section: A Challenges In Password Authenticationmentioning

confidence: 99%

A Secure and Scalable Behavioral Dynamics Authentication Model

Oladipo,

Awotunde,

Nicho

et al. 2023

IJACSA

View full text Add to dashboard Cite

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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%

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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“…A dedicated dataset for mobile and touch screen devices are still few in literature and they were not covering all the provided non-timing features we mentioned in the literature. So, we are in bad needs for public touch screen devices benchmarks [20][21][22][23][24][25][26][27][28].…”

Section: Benchmarking Datasets and Challengesmentioning

confidence: 99%

Enhanced Authentication System Performance Based on Keystroke Dynamics using Classification algorithms

Salem¹,

Sharieh²,

Sleit³

et al. 2019

KSII TIIS

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Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.

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Factors affecting keystroke dynamics for verification data collecting and analysis

Cited by 10 publications

References 7 publications

A Secure and Scalable Behavioral Dynamics Authentication Model

A Secure and Scalable Behavioral Dynamics Authentication Model

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

Enhanced Authentication System Performance Based on Keystroke Dynamics using Classification algorithms

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