VisGIN: Visibility Graph Neural Network on one-dimensional data for biometric authentication
Expert Systems with Applications, ISSN: 0957-4174, Vol: 237, Page: 121323
2024
- 4Citations
- 15Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The research community has extensively utilized biometrics, which is one of the most prominent sources of knowledge for authentication schemes in the rapidly advancing field of cybersecurity. Among various types of biometric information, an individual’s electrocardiogram (ECG) plot serves as an inimitable object as it cannot be faked intentionally. For large-scale applications, where granting access to genuine signal owners is necessary, deep learning is an essential component. In this context, the convergence between one-dimensional representations and Graph Neural Networks (GNN) includes prospective solutions. To capture the time-series data from a different perspective, hereby, we propose the VisGIN model which utilizes GINConv for the convolutional layers and passes Visibility Graphs (VG) as input. In parallel with our intuition, the findings of this study affirmed the fruition of the VisGIN approach, offering significant implications for the field of ECG authentication. By achieving an average classification accuracy of 99.76% in the evaluation of grant-access decisions, our VisGIN model demonstrates the effectiveness of graph machine learning models for time-series binary classification tasks, particularly in ECG authentication. As a result, our study provides a valuable advancement in enhancing the security and reliability of authentication systems. Researchers and practitioners can benefit from our work by leveraging the VisGIN model and its graph-based approach to bolster the accuracy and robustness of ECG authentication systems. Our code is available at https://github.com/AslantheAslan/visibility-gin.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0957417423018250; http://dx.doi.org/10.1016/j.eswa.2023.121323; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85170649995&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0957417423018250; https://dx.doi.org/10.1016/j.eswa.2023.121323
Elsevier BV
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