Volume 11 • Issue 2 • PP: 01–19 • 2026
Enhancing Identity Verification Reliability in Digital Environments Using Keystroke Dynamics and Dipper Throated Optimization
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Keystroke Dynamics Analysis (KDA) is a prominent behavioral biometric technique for continuous user authentication in digital environments. Yet, keystroke timing prediction remains challenging due to individual typing variability, temporal inconsistencies, and the tendency of machine learning models to overfit in high dimensional spaces when hyperparameters are poorly tuned. This study formulates the task as predicting keystroke timing intervals—dwell times, keydown–keydown latencies, and keyup–keydown latencies—for a fixed password sequence. We introduce a predictive framework that integrates the Dipper Throated Optimizer (DTO) with regression modeling, using a sequential dual optimization strategy: binary DTO (bDTO) first selects informative feature subsets, followed by standard DTO to fine-tune the hyperparameters of a Gradient Boosting Regressor (GBR). This design balances exploration and exploitation to address the complexity of optimization in behavioral biometric data. Experimental validation on the Keystroke Dynamics Benchmark Dataset demonstrates stepwise performance gains: the baseline GBR achieved an MSE of 0.014244, reduced to 0.004768 after bDTO based feature selection (66.5% improvement), and further refined to an MSE of 0.000003 with DTO hyperparameter tuning (99.97% relative improvement), a result interpreted with caution due to potential overfitting risks. The optimized model also attained R2 = 0.9824, Nash–Sutcliffe Efficiency = 0.9786, and Willmott Index = 0.9810, underscoring strong predictive agreement between observed and predicted timing intervals.
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