Machine Learning-Based Analysis of Free-Text Keystroke Dynamics
This work addresses cybersecurity needs for biometric authentication, but it is incremental as it builds on existing keystroke dynamics research.
The paper tackles user classification from free-text keystroke dynamics by implementing a novel deep learning model combining CNN and GRU, achieving results competitive with previous best methods.
The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Keystroke dynamics can be used to analyze the way that a user types based on various keyboard input. Previous work has shown that user authentication and classification can be achieved based on keystroke dynamics. In this research, we consider the problem of user classification based on keystroke dynamics features collected from free-text. We implement and analyze a novel a deep learning model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU). We optimize the resulting model and consider several relevant related problems. Our model is competitive with the best results obtained in previous comparable research.