Key result
The PulseOblivion session-based continuous authentication model using PPG signals achieved 95% authentication accuracy in multiple-session scenarios, outperforming state-of-the-art methods by >8.5%.
Why the study?
Previous PPG-based authentication techniques generated user signatures only during initial interaction, leaving inter-session PPG drifting unaddressed.
A novel session-based continuous authentication model using PPG signals significantly improves authentication accuracy and runtime speed compared to existing state-of-the-art methods.
PPG authentication advances show technical promise; leaves open clinical validation and deployment in cardiovascular settings.
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.
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Aly et al. (2023) studied this question. PulseOblivion session-based continuous authentication model vs. State-of-the-art baseline models was evaluated on Authentication accuracy. The PulseOblivion session-based continuous authentication model using PPG signals achieved 95% authentication accuracy in multiple-session scenarios, outperforming state-of-the-art methods by >8.5%.