Keystroke Dynamics Based on Machine Learning
Han-Chih Chang · 2021
The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Biometrics that utilize features derived from keystroke dynamics have been studied in this context. Keystroke dynamics can be used to analyze the way that a user types by monitoring various keyboard inputs. Previous work has considered the feasibility of user authentication and classification based on keystroke features. In this research, we analyze a wide variety of machine learning and deep learning models based on keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. We find that a model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU) preforms best in our experiments. This model also outperforms previous research in this field.