The Improved Biometric Identification of Keystroke Dynamics Based on Deep Learning Approaches

Area of Science:

• Computer Science
• Cybersecurity
• Biometrics

Background:

• Digital security concerns are rising, increasing the need for advanced identification methods.
• Biometric identification is gaining importance, but keystroke dynamics analysis is underutilized.
• Keystroke dynamics offers a distinctive and non-intrusive approach to user identification.

Purpose of the Study:

• To propose an innovative deep-learning methodology for keystroke dynamics-based identification.
• To demonstrate the effectiveness of deep learning in analyzing typing behaviors for biometrics.
• To highlight the potential of keystroke dynamics as a reliable biometric identifier.

Main Methods:

• Utilized open research datasets for training and validation.
• Developed and applied a novel deep-learning model.
• Extracted intricate patterns from user typing behaviors.

Main Results:

• The proposed deep-learning approach achieved superior performance compared to existing methods.
• Demonstrated high accuracy in identifying individuals based on keystroke dynamics.
• Showcased the capability of deep learning to uncover subtle typing patterns.

Conclusions:

• Deep learning is highly effective for keystroke dynamics analysis in biometric identification.
• Keystroke dynamics analysis holds significant untapped potential for enhancing digital security.
• This research advances the field of biometric identification through innovative deep learning applications.