Robust continuous authentication via multi-channel photoplethysmography signals: A wearable wristband solution for uncontrolled environments

The increasing prevalence of wearable devices has intensified security demands for robust continuous authentication (CA) systems to safeguard privacy and data integrity. Conventional one-time authentication methods fail to adapt to dynamic user behavior and environmental variations, leaving systems vulnerable to session hijacking and context-aware attacks. CA addresses these vulnerabilities by persistently monitoring biometric traits, thereby enabling adaptive security policies that balance usability and threat mitigation. Photoplethysmography (PPG) signals are uniquely suited for CA due to their non-invasive acquisition, temporal continuity, and anti-spoofing resilience. However, existing PPG-based CA systems rely on datasets collected under controlled conditions, lacking generalizability to real-world dynamics and sufficient security against multi-layer attacks. To tackle these, we propose a secure and robust CA framework leveraging multi-channel PPG signals from wearable wristbands. We first construct a multi-behavioral PPG dataset from 40 participants with 4-channel signals (dual green, red, and infrared light) under diverse activities. Then we design a multi-stage adaptive filtering pipeline that combines cascaded filters with Independent Component Analysis (ICA), effectively suppressing motion artifacts to improve signal quality. An end-to-end security scheme is integrated to ensure security and privacy of PPG data. Finally, we develop a hybrid Inception-LSTM network for authentication. Experimental results demonstrate a mean authentication accuracy of 94.89%, outperforming conventional single-channel baselines by 23.28% and exhibiting enhanced robustness against signal distortions.