Classification of Keystroke Dynamics with Deep Learning Models
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PDF downloads: 42
Keywords:
Keystroke, Security, DNN, CNN, Deep, LearningAbstract
Keystroke dynamics is a biometric application that determines the typing styles and behaviors of a
authentication processes because it is easy to collect data and has low
person or people. It is generally used in
implementation costs. Authentication methods play an important role in ensuring information security and
confidentiality. However, the inadequacy of biometric applications and the difficulties in determining data
based on people's behavior have necessitated the need for secure authentication and recognition systems
.
Therefore, keystroke dynamics were classified using deep learning models such as Deep Neural Networks
(DNN) and Convolutional Neural Networks (CNN) and a reliable authentication system was developed
.
Additionally, the results of the proposed deep learning models are presented comparatively. Authorization and
authentication systems, which are the most important elements of information security and cyber security
,
have been implemented by classifying and analyzing keystroke dynamics. In order to increase the success of
the system, it is aimed to determine the most optimum and accurate results with different hyperparameters
.
.
.
This study will have an important place in the development of authentication and recognition systems, which
play an important role in solving security problems such as authorization and data access by determining the
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References
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