Robust Continuous Authentication Using Cardiac Biometrics From Wrist-Worn Wearables

Traditional one-time user authentication is vulnerable to attacks when an adversary can obtain unauthorized privileges after a user's initial login. Continuous user authentication (CA) has recently shown its great potential by enabling seamless user authentication with few users' participation. We devise a low-cost system that can exploit users' pulsatile signals from photoplethysmography (PPG) sensors in commodity wearable devices to perform CA. Our system requires zero user effort and applies to practical scenarios that have nonclinical PPG measurements with human motion artifacts (MAs). We explore the uniqueness of the human cardiac system and develop adaptive MA filtering methods to mitigate the impacts of transient and continuous activities from daily life. Furthermore, we identify general fiducial features and develop an adaptive classifier that can authenticate users continuously based on their cardiac characteristics with little additional training effort. Experiments with our wrist-worn PPG sensing platform on 20 participants under practical scenarios demonstrate that our system can achieve a high CA accuracy of over 90% and a low false detection rate of 4% in detecting random attacks. We show that our MA mitigation approaches can improve the CA accuracy by around 39% under both transient and continuous daily activity scenarios.

Automated summary

Traditional one-time user authentication is vulnerable to attacks. Continuous user authentication (CA) utilizes users' pulsatile signals for authentication, reduces user participation, and improves accuracy through adaptive filtering methods. Experimental results demonstrate high accuracy and low false detection rate in practical scenarios, and the mitigation approaches further enhance accuracy.