PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals
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 ge...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10305593/ |
| _version_ | 1797629021798793216 |
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| author | Hussein A. Aly Roberto Di Pietro |
| author_facet | Hussein A. Aly Roberto Di Pietro |
| author_sort | Hussein A. Aly |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:48:29Z |
| format | Article |
| id | doaj.art-e19a8bc889884887ab287bcdb0787ef2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T10:48:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e19a8bc889884887ab287bcdb0787ef22023-11-14T00:01:13ZengIEEEIEEE Access2169-35362023-01-011112421312422710.1109/ACCESS.2023.332999310305593PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG SignalsHussein A. Aly0https://orcid.org/0000-0003-2384-6343Roberto Di Pietro1https://orcid.org/0000-0003-1909-0336Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarResilient Computing and Cybersecurity Center (RC3), Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi ArabiaIn 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.https://ieeexplore.ieee.org/document/10305593/Securitybiometric authenticationcontinuous authenticationPPGdeep autoencoders |
| spellingShingle | Hussein A. Aly Roberto Di Pietro PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals IEEE Access Security biometric authentication continuous authentication PPG deep autoencoders |
| title | PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals |
| title_full | PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals |
| title_fullStr | PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals |
| title_full_unstemmed | PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals |
| title_short | PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals |
| title_sort | pulseoblivion an effective session based continuous authentication scheme using ppg signals |
| topic | Security biometric authentication continuous authentication PPG deep autoencoders |
| url | https://ieeexplore.ieee.org/document/10305593/ |
| work_keys_str_mv | AT husseinaaly pulseoblivionaneffectivesessionbasedcontinuousauthenticationschemeusingppgsignals AT robertodipietro pulseoblivionaneffectivesessionbasedcontinuousauthenticationschemeusingppgsignals |