Enhancing Identity Verification Reliability in Digital

Environments Using Keystroke Dynamics and Dipper Throated

Optimization

Ebrahim A. Mattar1,*

1 College of Engineering, University of Bahrain, Bahrain

Received: January 29, 2026 Revised: March 28, 2026 Accepted: May 19, 2026 ⋆ Corresponding author

ABSTRACT

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.

Keywords: Keystroke Dynamics Analysis Behavioral Biometrics Gradient Boosting Regressor Metaheuristic

Optimization Dipper Throated Optimizer

1. INTRODUCTION

KDA is a primary behavioral biometric subfield that proves

essential for individual recognition through studied keyboard

patterns [1] . The main distinction between behavioral biometrics

is its ability to use device interaction temporal aspects,

unlike traditional biometric systems, which depend on physical

attributes including fingerprints, iris patterns, and facial

features. Keystroke dynamics stands out due to its ability

to gather data without disturbing users since it acquires information

easily and continues monitoring without needing

specialized equipment. Keystroke dynamics require analyzing

timing features where the key press time is called dwell

time and the time between successive keystrokes is flight

time [2]. Application timing features analyze user typing

patterns, providing benefits for identity verification services,

fraud prevention systems, and human-computer interaction

(HCI).