Applying the Douglas–Peucker Algorithm in Online Authentication of Remote Work Tools for Specialist Training in 10.00.00 “Information Security” Integrated Group of Specialties

— With educational systems shifting to distance learning and the trend towards remote work growing, an urgent need has arisen to develop reliable biometric identification and authentication technologies to verify employees working remotely. Such technologies can provide a high degree of protection a...

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Bibliographic Details
Published in:Automatic documentation and mathematical linguistics Vol. 58; no. Suppl 4; pp. S265 - S268
Main Authors: Uymin, A. G., Grekov, V. S.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.12.2024
Springer Nature B.V
Subjects:
Accuracy
Acknowledgment
Algorithms
Authentication
Authenticity
Biometric identification
Biometrics
Computer Science
Data processing
Distance learning
Educational systems
Educational technology
Efficiency
Gesture recognition
Gestures
Human-computer interaction
Information Storage and Retrieval
Interfaces
Machine learning
Neural networks
Optimization
Personal computers
Recognition
Security systems
Simplification
Training
User interfaces
Work
Work at home
data preprocessing
HID
mouse gesture trajectories
biometric identification
remote work
Douglas–Peucker algorithm
neural network
data optimization
authentication
distance learning
ISSN:0005-1055, 1934-8371
Online Access:Get full text
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Summary:— With educational systems shifting to distance learning and the trend towards remote work growing, an urgent need has arisen to develop reliable biometric identification and authentication technologies to verify employees working remotely. Such technologies can provide a high degree of protection and usability, making their development and optimization extremely important. The issue is that accuracy and efficiency of mouse gesture recognition systems need to be improved without any specialized devices used and in the shortest possible time. This requires efficient preprocessing of such gestures to simplify their trajectories while preserving their key features. The Douglas–Peucker algorithm is proposed to be used for preliminary processing of mouse gesture trajectory data. This algorithm allows significantly reducing the number of points in the trajectories, simplifying them while preserving the principal shape of the gestures. The data with simplified trajectories are then used to train neural networks. The experimental part of the work showed that, when applied, the Douglas–Peucker algorithm allows for a 60% reduction in the number of points on the trajectories, increasing the gesture recognition accuracy from 70 to 82%. Such data simplification contributes to speeding up the neural networks' training process and improving their operational efficiency. The study confirmed the efficiency of using the Douglas–Peucker algorithm for preliminary data processing in mouse gesture recognition problems. The results can be applied to develop more intuitive and adaptive user interfaces. In addition, directions for further research, including optimization of the algorithm’s parameters for different types of gestures and exploring the possibility of combining it with other machine learning methods, are proposed.
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ISSN:0005-1055
1934-8371
DOI:10.3103/S0005105525700323