Developing of keystroke dynamics analyzing systems using a presentation based on state contexts

This aim of the article is to discusses the main static and dynamic user identification methods by keystroke dynamics. As part of the research, a generalized way of representing the process of typing on the keyboard based on the sequential change of the keyboard state was proposed. The definition of the keyboard state context, which is the basis for the dynamic identification procedure, is formulated. The proposed approach will make it possible to apply a variety of static identification methods, significantly expanding the set of methods used for dynamic user identification by keystroke dynamics.

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User identification through keystroke dynamics as part of automated proctoring systems

National Interests Priorities and Security ◽

10.24891/ni.16.3.582 ◽

2020 ◽

Vol 16 (3) ◽

pp. 582-596

Author(s):

A.R. Abzalov ◽

A.V. Zhiganov ◽

R.R. Samigullina

Keyword(s):

Online Courses ◽

Comparison Method ◽

Keystroke Dynamics ◽

User Identification ◽

Effective Technique ◽

Identification Methods ◽

Statistical Level ◽

Continuous User ◽

Percent Accuracy ◽

Emotional Condition

Subject. Popular online courses and testing programs integrate into correspondence education systems, which are more often than not based on automated proctoring. What makes the latter vulnerable is user identification. Objectives. We examine user identification methods through keystroke dynamics and devise a more accurate and effective technique for user identification through keystroke dynamics. Methods. The article sets out a three-tiered model for identifying users more accurately not only in automated proctoring environments, but also in critically sensitive locations. Results. We had an experiment, which showed a 97.5 percent accuracy of user identification. We significantly reduced illegitimate users at the statistical level of the three-tiered model. Conclusions and Relevance. Following the study, it is possible to develop a logic comparison method for higher accuracy. It will serve for creating a more refined model, which would accommodate for distinctions of each user and some deviations of users’ emotions. This would contribute to continuous user identification systems to monitor their emotional condition at critical locations.

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CONTINUOUS KEYSTROKE DYNAMICS USER IDENTIFICATION USING STATE CONTEXT REPRESENTATION

XXI Century: Resumes of the Past and Challenges of the Present plus ◽

10.46548/21vek-2020-0952-0012 ◽

2020 ◽

Vol 9 (51) ◽

Author(s):

D.V Pashchenko ◽

◽

E.A Balzannikova ◽

Keyword(s):

Keystroke Dynamics ◽

User Identification ◽

State Context

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Methods of long-term and short-term adaptation for biometric image of keystroke dynamics

LAPLAGE EM REVISTA ◽

10.24115/s2446-6220202173d1737p.450-457 ◽

2021 ◽

Vol 7 (3D) ◽

pp. 450-457

Author(s):

Dmitry V. Pashchenko ◽

Dmitry A. Trokoz ◽

Alexey I. Martyshkin ◽

Elena A. Balzannikova

Keyword(s):

Working Capacity ◽

Keystroke Dynamics ◽

Short Term ◽

User Identification ◽

New Device ◽

Short Term Adaptation ◽

Long Time

This article discusses one of the main problems of user identification by keyboard handwriting - short-term changes in the keystroke dynamics of users in connection with its psychophysical state, as well as changes over a long time associated with the formation of keystroke dynamics by a new user or when switching to a new device. A method for determining the phase of working capacity by the time characteristics of the keystroke dynamics is proposed.

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Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Complexity ◽

10.1155/2017/8197602 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Lan Wang ◽

Yu Cheng ◽

Jinglu Hu ◽

Jinling Liang ◽

Abdullah M. Dobaie

Keyword(s):

System Identification ◽

Nonlinear System ◽

Radial Basis Function Network ◽

Nonlinear System Identification ◽

Support Vector ◽

Identification Procedure ◽

Identification Methods ◽

Nonlinear Parameter Identification ◽

And Control ◽

Function Network

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

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Instantaneous Identification of Polynomial Nonlinearity Based on Volterra Series Representation

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.293-294.703 ◽

2005 ◽

Vol 293-294 ◽

pp. 703-710 ◽

Cited By ~ 8

Author(s):

Giacomo V. Demarie ◽

Rosario Ceravolo ◽

Alessandro de Stefano

Keyword(s):

Structural Engineering ◽

Volterra Series ◽

Series Representation ◽

Consistent Estimate ◽

Short Time Fourier Transform ◽

Dynamic Identification ◽

Polynomial Nonlinearity ◽

Definition Of ◽

Short Time

In structural engineering applications a sufficient quantity of experimental data to be able to achieve a consistent estimate of nonlinear quantities is seldom available: this applies in particular when the structures are to be tested in situ. This report discusses the definition of instantaneous estimators to be used in the dynamic identification of invariant nonlinear systems on the basis of Short-Time Fourier Transform representation of excitation and system’s response and within the framework of a Volterra series representation of the input/output relationship. An estimation of the parameters of a dynamic system can be worked out from the evolution of such instantaneous estimators.

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