RF Fingerprinting-Based IoT Node Authentication Using Mahalanobis Distance Correlation Theory

Abstract

The wireless Internet of Things (IoT) node authentication approaches also used Radio Frequency (RF) fingerprinting or physical unclonable features (PUF) of IoT devices for node authentication. Machine learning based models play vital role in these approaches. In this letter, we introduce an effective and novel IoT node authentication approach using Mahalanobis Distance correlation and Chi-square distribution theories. Further, it has lower computational time to determine node authenticity. The comparative results of our proposal with three recent machine learning based approaches and PUF based approaches are promising which validates the effectiveness and the novelty of our proposal.