Real-Time Behavioral Biometrics and Continuous Authentication Framework for Secure Financial Transaction Ecosystems

Abstract

Digital financial platforms rely heavily on session-based authentication mechanisms that verify user identity only at discrete checkpoints, leaving systems vulnerable to post-login account takeover attacks. Continuous authentication based on behavioral biometrics has emerged as a promising solution for maintaining persistent identity assurance throughout active sessions. This work proposes a real-time behavioral biometric framework for continuous authentication using keystroke dynamics and sequential monitoring of interaction patterns. The proposed method models user-specific behavioral structure through reconstruction consistency in a low-dimensional subspace learned from enrollment data. Incoming interaction events are evaluated in real time using reconstruction error, and a sliding temporal window aggregates behavioral deviation to support stable decision making. User-specific thresholds derived from enrollment statistics enable adaptive sensitivity control, while takeover detection latency is explicitly quantified to assess operational security effectiveness. Experiments were conducted using the CMU Keystroke Dynamics benchmark under simulated session takeover scenarios. Performance was evaluated using verification metrics including false acceptance rate, false rejection rate, equal error rate, and temporal detection delay. Results demonstrate that the proposed framework achieves reliable identity discrimination while enabling rapid detection of behavioral takeover events with low latency and stable threshold behavior. The findings confirm that continuous behavioral monitoring can significantly reduce the exposure window of compromised sessions, even under unimodal biometric sensing. The proposed framework provides a computationally efficient and deployment-ready approach for strengthening real-time security in financial transaction ecosystems.