PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals
<p dir="ltr">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 int...
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| مؤلفون آخرون: | |
| منشور في: |
2023
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513527385948160 |
|---|---|
| author | Hussein A. Aly (17984050) |
| author2 | Roberto Di Pietro (16875987) |
| author2_role | author |
| author_facet | Hussein A. Aly (17984050) Roberto Di Pietro (16875987) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hussein A. Aly (17984050) Roberto Di Pietro (16875987) |
| dc.date.none.fl_str_mv | 2023-11-02T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3329993 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/PulseOblivion_An_Effective_Session-Based_Continuous_Authentication_Scheme_Using_PPG_Signals/25239736 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Authentication Data models Biometrics (access control) Sensors Biological system modeling Security Electrocardiography continuous authentication PPG deep autoencoders |
| dc.title.none.fl_str_mv | PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3329993" target="_blank">https://dx.doi.org/10.1109/access.2023.3329993</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c546a2c29f4d3b4d3a8734c7bcc3978d |
| identifier_str_mv | 10.1109/access.2023.3329993 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239736 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG SignalsHussein A. Aly (17984050)Roberto Di Pietro (16875987)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringAuthenticationData modelsBiometrics (access control)SensorsBiological system modelingSecurityElectrocardiographycontinuous authenticationPPGdeep autoencoders<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3329993" target="_blank">https://dx.doi.org/10.1109/access.2023.3329993</a></p>2023-11-02T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3329993https://figshare.com/articles/journal_contribution/PulseOblivion_An_Effective_Session-Based_Continuous_Authentication_Scheme_Using_PPG_Signals/25239736CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252397362023-11-02T09:00:00Z |
| spellingShingle | PulseOblivion: An Effective Session-Based Continuous Authentication Scheme Using PPG Signals Hussein A. Aly (17984050) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Authentication Data models Biometrics (access control) Sensors Biological system modeling Security Electrocardiography continuous authentication PPG deep autoencoders |
| status_str | publishedVersion |
| 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 | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Authentication Data models Biometrics (access control) Sensors Biological system modeling Security Electrocardiography continuous authentication PPG deep autoencoders |