دورية أكاديمية
Explaining vulnerabilities of heart rate biometric models securing IoT wearables
| العنوان: | Explaining vulnerabilities of heart rate biometric models securing IoT wearables |
|---|---|
| المؤلفون: | Chi-Wei Lien, Sudip Vhaduri, Sayanton V. Dibbo, Maliha Shaheed |
| المصدر: | Machine Learning with Applications, Vol 16, Iss , Pp 100559- (2024) |
| بيانات النشر: | Elsevier, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Cybernetics LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | IoT authentication model, Biometric authentication, Heart rate model, Implicit authentication, Machine learning application, Security, Cybernetics, Q300-390, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | In the field of health informatics, extensive research has been conducted to predict diseases and extract valuable insights from patient data. However, a significant gap exists in addressing privacy concerns associated with data collection. Therefore, there is an urgent need to develop a machine-learning authentication model to secure the patients’ data seamlessly and continuously, as well as to find potential explanations when the model may fail. To address this challenge, we propose a unique approach to secure patients’ data using novel eigenheart features calculated from coarse-grained heart rate data. Various statistical and visualization techniques are utilized to explain the potential vulnerabilities of the model. Though it is feasible to develop continuous user authentication models from readily available heart rate data with reasonable performance, they are affected by factors such as age and Body Mass Index (BMI). These factors will be crucial for developing a more robust authentication model in the future. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2666-8270 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666827024000355; https://doaj.org/toc/2666-8270 |
| DOI: | 10.1016/j.mlwa.2024.100559 |
| URL الوصول: | https://doaj.org/article/576d7edd35004f849981c8c72db6d277 |
| رقم الأكسشن: | edsdoj.576d7edd35004f849981c8c72db6d277 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26668270 |
|---|---|
| DOI: | 10.1016/j.mlwa.2024.100559 |