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Deep Learning Model for Securing IoT Configurations for End-to-End Data Authentication

Radhika Singh

Abstract


Electrocardiograms (ECGs) have gained widespread acceptance as a medium for validating animations in numerous security applications, particularly in new and emerging technologies, in comparison to other biometrics. In particular, the preprocessing, feature extraction, and classification procedures of our proposed method are integrated into a single unit, and each ECG signal from the database is directly fed into a convolution neural network (CNN) model to be classified as an accepted or rejected class. Additionally, we design our authentication system to be low-cost and low-latency, making it suitable for use with Fog computing platforms. We applied our proposed model to standard ECG signals from the Physikalisch-Technische Bundesanstalt (PTB) database in order to verify its suitability for use in real-time authentication systems. The results for accuracy, precision, recall, and F1-score are 99.50%, 99.73%, 100%, and 99.78%, respectively. In addition, we discuss the model's performance in relation to more recent methods based on conventional machine and deep learning methods.

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