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Detecting Characteristics Based on Keystroke Dynamics Analysis Using Machine Learning

Almuhaya, Rahaf
; 
Alharbi, Wejdan
; 
Abdullah, Kawthar
; 
Joud, Alansari
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Type
Capstone
Supervisor
Abed, Fidaa
Subject
Keystroke dynamics analysis, User characteristics, Machine learning, Online keyboard, Behavioral biometrics, User behavior modeling, Personalized user interfaces, Human-computer interaction, Feature extraction
Date
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Computer Science - Undergraduate works
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Capstone Project.pdf
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Abstract
This project aims to detect several characteristics of an individual based on their typing behavior. Data is collected through a pre-programmed online keyboard, capturing metrics such as flight time, key press and release timing, and other keystroke dynamics. The collected dataset is analyzed using machine learning algorithms to identify distinct typing patterns and extract relevant features. Based on this analysis, the trained machine learning model classifies individuals into two categories: above or under the age of 18. This classification approach leverages typing behavior to provide an innovative method for age-based categorization, with potential applications in user authentication and personalization.
Department
Computer Science
Publisher
Effat University
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Open Access
https://repository.effatuniversity.edu.sa/handle/20.500.14131/2412
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