LSTM-based ensemble models for keystroke dynamics authentication: integrating explainable AI for transparency.

The increasing insecurity of traditional methods such as passwords and PINs has raised significant interest in behavioral biometrics. Keystroke Dynamics (KSD), which relies on the unique manner in which an individual types, is a promising candidate for continuous and unobtrusive authentication. This study presents a hybrid model for KSD that combines a Long Short-Term Memory (LSTM) network with an ensemble of Random Forest, XGBoost, and Multilayer Perceptron classifiers using a soft-voting strategy. The model incorporates advanced feature engineering techniques to capture meaningful typing patterns and user-specific behavioral characteristics. The temporal features extracted by the LSTM are effectively classified by the ensemble model, resulting in strong authentication performance on the KDA Keystroke Dynamics dataset. The proposed approach achieved an accuracy of 94.75%, with a recall of 94.89%, precision of 94.75%, F1-score of 94.76%, and MCC of 94.65%. Furthermore, the model demonstrated efficient real-time performance with a throughput of 16,092.93 samples per second and a detection time of only 0.0621 ms per sample. In addition, SHAP-based explainability analysis enhanced model interpretability by identifying the most influential keystroke features contributing to authentication decisions. Overall, the proposed framework provides a secure, scalable, interpretable, and computationally efficient solution for real-time keystroke dynamics-based user authentication.