High Accuracy RF-PUF for EM Security through Physical Feature Assistance using Public Wi-Fi Dataset

In this work, using physical features extracted from RF nonidealities in communicated EM signals, we show that radio frequency physical unclonable function (RF-PUF) performs much better compared to a solely convolutional neural network (CNN) based authentication method, ORACLE. For the static and quasi-static channels, respectively, we achieve 96% and 100% accuracy for RF-PUF compared to 87.13% and 98.6% accuracy for ORACLE. For the first time, RF-PUF has been applied for Wi-Fi devices to show that >95% accuracy can be achieved for a wide range of transmitter and receiver separation from 2ft to 62ft both for the static and quasi-static channel, showing a peak of ~100% within 38ft range for the static case. The design space has been explored in detail. Finally, the concept of RF-PUF has been applied for clustering to detect safe-listed devices.