Machine Learning Meets EEG: A Novel Approach to PGA-Based Authentication Systems
The development of secure and effective identification/authentication systems is an ongoing challenge in the field of computer science, as new techniques and methods continue to emerge. In the context of this ever-evolving landscape, we propose a new multi-factor authentication system. The proposed...
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| Vydáno v: | Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) s. 924 - 929 |
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18.12.2024
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| ISSN: | 1946-0759 |
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| Abstract | The development of secure and effective identification/authentication systems is an ongoing challenge in the field of computer science, as new techniques and methods continue to emerge. In the context of this ever-evolving landscape, we propose a new multi-factor authentication system. The proposed system combines Picture Gesture Authentication (PGA) with simultaneous acquisition of users' brain signals (EEG) to enhance security. To assess the efficacy of the proposed model, we collected data from 14 individuals, each of whom participated in 20 trials to create a single-person authentication system. Features were extracted and multiple machine learning algorithms were trained and tested in three different scenarios. The first scenario utilized the created dataset to find the most effective machine learning algorithms and features; the second applied augmentation techniques for a more balanced dataset; the third used only the most important features. The results were encouraging, indicating that the proposed model works well in enhancing the level of security. |
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| AbstractList | The development of secure and effective identification/authentication systems is an ongoing challenge in the field of computer science, as new techniques and methods continue to emerge. In the context of this ever-evolving landscape, we propose a new multi-factor authentication system. The proposed system combines Picture Gesture Authentication (PGA) with simultaneous acquisition of users' brain signals (EEG) to enhance security. To assess the efficacy of the proposed model, we collected data from 14 individuals, each of whom participated in 20 trials to create a single-person authentication system. Features were extracted and multiple machine learning algorithms were trained and tested in three different scenarios. The first scenario utilized the created dataset to find the most effective machine learning algorithms and features; the second applied augmentation techniques for a more balanced dataset; the third used only the most important features. The results were encouraging, indicating that the proposed model works well in enhancing the level of security. |
| Author | Koukopoulos, Dimitrios Katoikos, Ioannis Fidas, Christos A. |
| Author_xml | – sequence: 1 givenname: Ioannis surname: Katoikos fullname: Katoikos, Ioannis email: i.katoikos@ac.upatras.gr organization: University of Patras,Department of Electrical and Computer Engineering,Patras,Greece – sequence: 2 givenname: Christos A. surname: Fidas fullname: Fidas, Christos A. email: fidas@upatras.gr organization: University of Patras,Department of Electrical and Computer Engineering,Patras,Greece – sequence: 3 givenname: Dimitrios surname: Koukopoulos fullname: Koukopoulos, Dimitrios email: dkoukopoulos@upatras.gr organization: University of Patras,Department of History and Archaeology,Agrinio,Greece |
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| Snippet | The development of secure and effective identification/authentication systems is an ongoing challenge in the field of computer science, as new techniques and... |
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| SubjectTerms | Authentication Brain modeling Electroencephalography Electronics packaging Feature extraction Graphical/visual passwords Human-centered computiug→ Laboratory experiments Machine learning Machine learning algorithms Multi-factor authentication Recording Security security and privacy → Biometrics Visualization |
| Title | Machine Learning Meets EEG: A Novel Approach to PGA-Based Authentication Systems |
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