Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics
This work addresses user authentication for security applications, but it is incremental as it builds on existing methods with optimization and comparison.
The paper tackled user authentication and identification using fixed-text keystroke dynamics by applying and optimizing various machine learning and deep learning techniques, finding that XGBoost and MLP models outperformed previous comparable research.
Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. Previous work has demonstrated the feasibility of user authentication and identification utilizing keystroke dynamics. In this research, we consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. We find that models based on extreme gradient boosting (XGBoost) and multi-layer perceptrons (MLP)perform well in our experiments. Our best models outperform previous comparable research.