Statistical Analysis Based Feature Selection Enhanced RF-PUF with >99.8% Accuracy on Unmodified Commodity Transmitters for IoT Physical Security

TL;DR

This paper demonstrates that RF-PUF, using a new statistical feature set, can reliably authenticate commodity IoT devices with over 99.8% accuracy, without device modifications, through extensive experimental validation.

Contribution

It is the first to evaluate RF-PUF on off-the-shelf commodity devices, introducing a new feature set and analyzing robustness and performance in real-world conditions.

Findings

Achieved >99.8% accuracy with sufficient data and model capacity.

Collected and released a public dataset from 30 off-the-shelf modules.

Established RF-PUF as a highly reliable authentication method with a detection probability of 0.9987.

Abstract

Due to the diverse and mobile nature of the deployment environment, smart commodity devices are vulnerable to various attacks which can grant unauthorized access to a rogue device in a large, connected network. Traditional digital signature-based authentication methods are vulnerable to key recovery attacks, CSRF, etc. To circumvent this, RF-PUF had been proposed as a promising alternative that utilizes the inherent nonidealities of the devices as physical signatures. RF-PUF offers a robust authentication method that is resilient to key-hacking methods due to the absence of secret key requirements and does not require any additional circuitry on the transmitter end, eliminating additional power, area, and computational burden. In this work, for the first time, we analyze the effectiveness of RF-PUF on commodity devices, purchased off-the-shelf, without any modifications whatsoever. Data were collected from 30 Xbee S2C modules and released as a public dataset. A new feature has been engineered through statistical property analysis. With a new and robust feature set, it has been shown that 95% accuracy can be achieved using only ~1.8 ms of test data, reaching >99.8% accuracy with more data and a network of higher model capacity, without any assisting digital preamble. The design space has been explored in detail and the effect of the wireless channel has been determined. The performance of some popular ML algorithms has been compared with the NN approach. A thorough investigation on various PUF properties has been done and both intra and inter-PUF distances have been calculated. With extensive testing of 41238000 cases, the detection probability for RF-PUF for our data is found to be 0.9987, which, for the first time, experimentally establishes RF-PUF as a strong authentication method. Finally, the potential attack models and the robustness of RF-PUF against them have been discussed.