VisGIN: Visibility Graph Neural Network on one-dimensional data for biometric authentication

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.

Automated summary

Biometric information, such as an individual's electrocardiogram (ECG), is extensively used in the field of cybersecurity for authentication schemes. The VisGIN model, which utilizes Graph Neural Networks (GNN) and Visibility Graphs (VG), shows promising results in ECG authentication, achieving an average classification accuracy of 99.76%. This study provides a valuable advancement in enhancing the security and reliability of authentication systems.