PulseOblivion: An Effective Session-Based Continuous Authentication
Scheme Using PPG Signals
Abstract
In this paper, we propose a novel session-based continuous
authentication model using photoplethysmography (PPG). Unlike previous
PPG-based authentication techniques that generate user signatures only
during the initial interaction, our session-based approach tackles inter
session PPG drifting by generating a user signature at the start of each
session. Our model is composed by two modules: Firstly, heavy deep
autoencoders (AE) are utilized for feature extraction and, secondly, a
lightweight Local Outlier Factor (LOF) is employed for user
authentication. Additionally, we introduce a continuous updating system
for the LOF model, which automatically recovers from security breaches
and can enhance authentication accuracy by more than 9%. Our
experiments show that in a single-session scenario, our model achieves
authentication accuracies of 93.5% and 91.8% on the CapnoBase and
BIMDC benchmarking datasets, respectively, outperforming the
state-of-the-art baseline model by 3.2% and 1.6% on both datasets,
respectively. In multiple-session scenarios, our scheme attains an
authentication accuracy of 95% when tested on the BioSec2 dataset,
effectively mitigating inter-session PPG drifting and achieving an
advantage of more than 8.5% in authentication accuracy over the
state-of-the-art method. In terms of execution speed, our solution is
seven times faster at runtime compared to competing state-of-the-art
solutions.