Mining a new biometrics to improve the accuracy of keystroke dynamics-based authentication system on free-text

Tsai, Cheng Jung; Shih, Kuen Jhe

doi:10.1016/j.asoc.2019.03.033

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…The Table 3 shows the characteristics of the databases used in previous scientific research, in order to be able to compare them with the characteristics of the data set obtained in this research. The characteristics were being published and centralized in the paper [29]. In the last line of the table are the characteristics of the data set in this paper.…”

Section: Comparison Of the Related Workmentioning

confidence: 99%

Shared Data Set for Free-Text Keystroke Dynamics Authentication Algorithms

Iapa¹,

Cretu²

2021

Preprint

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Identifying or authenticating a computer user are necessary steps to keep systems secure on the network and to prevent fraudulent users from accessing accounts. Keystroke dynamics authentication can be used as an additional authentication method. Keystroke dynamics involves in-depth analysis of how you type on the keyboard, analysis of how long a key is pressed or the time between two consecutive keys. This field has seen a continuous growth in scientific research. In the last five years alone, about 10,000 scientific researches in this field have been published. One of the main problems facing researchers is the small number of public data sets that include how users type on the keyboard. This paper aims to provide researchers with a data set that includes how to type free text on the keyboard by 80 users. The data were collected in a single session via a web platform. The dataset contains 410,633 key-events collected in a total time interval of almost 24 hours. In similar research, most datasets are with texts written by users in English. The language in which the users wrote for this research is Romanian. This paper also provides an extensive analysis of the data set collected and presents relevant information for the analysis of the data set in future research.

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Section: Comparison Of the Related Workmentioning

confidence: 99%

Shared Data Set for Free-Text Keystroke Dynamics Authentication Algorithms

Iapa¹,

Cretu²

2021

Preprint

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“…The four most generally considered keystroke dynamic features are illustrated as in figure 1, which are defined by keystroke times of button presses and button releases, i.e., PR (press-release), PP (press-press), RP (release-press) and RR (release-release) times. More precisely, for example, the PR time is the time interval between pressing and releasing of the same key [6]. In addition to these four keystroke dynamic features, other keystroke biometrics had been proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The ''pause rhythm'' method was proposed and required users to add some pauses when entering their passwords. However, since one had to remember many extra information for the ''new'' password, such as where and the number of pauses inserted, and time length of each pause, this method was not only to conflict with the aim that the biometric features should be collected naturally, but also increase users' input burden, so it was not been widely adopted [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Real – Time Biometric Authentication Based on Key Stroke Dynamics

2020

jcr

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In this paper we discuss the ability to implement keystroke dynamics in a simple way by taking few, but effective, features and run the authentication session in a real-time manner. MATLAB 2019 is used to implement the keystroke biometrics and 15 people were included in the test aging from 20 to 30 years, and 10 samples are taken from each volunteer to train the Back Propagation Neural Network (BBNN) in order to classify the various entered patterns according to the obtained features. The results are satisfying were the FRR has a value of 27% and FAR is about 18%. This approach, named Simple but Effective (SbE), can be done using nonconvenient features which in turn makes it unique, in addition to its simplicity that makes it relatively consumes less resources than previous experiments.

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“…Several research works like keypress frame [14]. biometric data mining, [27]. etc., are done to maximize the exactness measure of authenticated user classification.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Optimized Mouse and Keystroke Dynamics Framework for Continuous Non-Intrusive User Authentication

Thomas¹,

Mathew

2021

Wireless Pers Commun

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In recent years, user authentication based on mouse and keystroke dynamic is the most wanted topic to identify the external user and to secure information. Additionally based on the movement of the mouse and typing speed of keystroke the correct user was identified. But the problems of existing approaches are complicated data, data error, and malicious events. To overcome these threats, a novel cat recurrent neural model (CRNM) is proposed to identify the correct user and improve the accuracy rate. In this work, the CRNM approach is introduced to minimize the error rate, to detect unauthorized users by analyzing the user's mouse and keystroke dynamic. Consequently, the trained datasets verify the inputs and identify the correct user. Thus the proposed CRNM has been implemented in the python framework, to identify the correct user. Moreover, the proposed model is validated with other existing deep learning models in terms of accuracy, false acceptance rate (FAR), F-measure, recall, false negative rate (FNR), precision, and error rate.

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Mining a new biometrics to improve the accuracy of keystroke dynamics-based authentication system on free-text

Cited by 17 publications

References 42 publications

Shared Data Set for Free-Text Keystroke Dynamics Authentication Algorithms

Shared Data Set for Free-Text Keystroke Dynamics Authentication Algorithms

Real – Time Biometric Authentication Based on Key Stroke Dynamics

An Efficient Optimized Mouse and Keystroke Dynamics Framework for Continuous Non-Intrusive User Authentication

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