Behavioral Biometrics-Powered Continuous Authentication for Zero-trust Remote Work Environments: A Multi-factor Identity Verification Framework

This research presents a behavioral biometrics-powered continuous authentication framework designed for zero-trust remote work environments in healthcare. The study integrated keystroke dynamics, mouse movement patterns, and contextual risk factors into a multimodal system enabling seamless, real-time identity verification. Using a fused dataset of 3,600 samples from 24 users derived from public keystroke and mouse-dynamics repositories and augmented with healthcare-specific synthetic data where 79 behavioral features were engineered and normalized. Six machine learning and deep learning models were trained, including Random Forest, XGBoost, Support Vector Machine (SVM), LSTM, CNN-LSTM, and CNN, with Random Forest and XGBoost achieving the best performance at 98.25% accuracy, 0% Equal Error Rate (EER), and an AUC-ROC of 1.0000. The framework operated frictionlessly, with inference times below 2.6 ms, ensuring zero disruption to clinical workflows. Dynamic trust scoring enabled adaptive access control, while attack simulations across six threat scenarios yielded a 90.3% detection rate, including 100% for insider threats and zero-effort impersonation. Full compliance with HIPAA standards was validated through continuous monitoring, audit logging, and real-time threat response. The system outperformed traditional authentication methods in accuracy, usability, and security resilience. Despite strong results, limitations include constrained user diversity and simulated environments. The framework advances zero-trust principles by providing passive, high-precision authentication suitable for distributed healthcare systems. Future work should focus on longitudinal field deployment and adaptive modeling to address behavioral drift and emerging threats.