الفهرس 
CHAPTER 1 INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
User authentication is vital for security in digital services. Organizations now use
Multi-Factor Authentication (MFA) to enhance security, using multiple identity checks
instead of just relying on Single Factor Authentication (SFA). Keystroke dynamics,
analyzing how users type, provides a cost-effective and hardware-independent way to
verify users during authentication.
This thesis presents two methodologies to improve user authentication using
keystroke dynamics. The first methodology introduces an efficient technique using
quantile transformation to normalize data distribution, improving accuracy. The main
classifier is Histogram Gradient Boosting, achieving high accuracy on the Carnegie
Mellon University (CMU) dataset.
The second methodology builds on this, proposing an optimized convolutional
neural network (CNN) that includes data synthesis and quantile transformation for
better feature extraction. The approach uses ensemble learning and achieves exceptional
accuracy on the CMU dataset.
Combining these approaches creates a comprehensive framework for keystroke
dynamics-based user authentication, contributing to strong authentication systems.
Practical implications include safeguarding online services and improving security in
various domains.