Deep learning-based behavioral drift modeling for continuous biometric authentication

Abstract

Ascending technologies like the concepts of robotics, artificial intelligence (AI), the concepts of smart devices and the Internet of Things (IoT) have been assimilated into traditional and indispensable physical, biological and digital systems incubating the fourth industrial revolution. Following the era where the attainability of AI has become ubiquitous, vitality has risen to integrate the smartness of AI into traditional authentication systems in order to strengthen the security and develop a resilient and robust barrier between the infiltrator and the delicate systems. The integration of unimodal physiological biometrics along with the previous password systems served the purpose to a certain extent. But the widespread use of AI has given the intruders an easier access to breach the seclusion of systems, raising a massive question for the security, especially in this era of digital transactions, banking, healthcare systems and academic assessments. There comes the necessity of initiating the utilization of behavioral biometric authentication that integrates the behavioral drift of individuals so that impostors relying on static templates and physiological traits like fingerprints are recognized and the system remains decontaminated. In this regard, this research proposes a two-level keystroke dynamics authentication system that provides defense-in-depth security through continuous behavioral drift monitoring. Level 1 employs fixed-text password authentication using Temporal Convolutional Networks (TCN) with contrastive learning and population-based training (PBT), achieving 3.77% EER for high-security login verification. Upon successful authentication, Level 2 continuously monitors user typing during natural free-text sessions using a 4-Model Deep Learning Ensemble. If suspicious behavioral drift is detected during monitoring, the system re-authenticates the user by returning to Level 1, providing a feedback loop against session hijacking and behavioral anomalies. The system explicitly models long-term behavioral drift through the fixed-text component’s PBT mechanism, which adapts to temporal variations in password typing patterns. This research leverages free-text (Buffalo dataset) and fixed-text (GREYC dataset) keystroke dynamics datasets, extracting temporal and spatial features including hold times, up-down latencies, down-down intervals, and trigram patterns. The final system achieves 96%+ accuracy for fixed-text authentication (3.77% EER) and 83.53% True Acceptance Rate (TAR) at 5% False Acceptance Rate (FAR) for free-text continuous authentication (10.05% EER), demonstrating superior performance and strong resistance to impostor attacks through continuous behavioral monitoring.