Statistical Analysis Based Feature Selection Enhanced RF-PUF With >99.8% Accuracy on Unmodified Commodity Transmitters for IoT Physical Security - Bari , Md Faizul; Agrawal , Parv; Chatterjee , Baibhab; Sen , Shreyas (Frontiers in Electronics, 2022)
Due to the diverse and mobile nature of the deployment environment, smart and connected commodity devices are vulnerable to various attacks which can grant unauthorized access to a rogue device in a large, connected network and lead to data theft and malicious activities. Traditional digital signature-based authentication methods are vulnerable to key recovery attacks, cross-site request forgery, etc. To circumvent the inherent weakness of the digital signature-based authentication system, 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 the need for 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 used as transmitters 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 milliseconds of test data fed into a lightweight neural network (with 10 neurons in 1 layer), reaching >99.8% accuracy with more data and network of higher model capacity (1 layer with 70 neurons), for the first time in literature without any assisting digital preamble. The design space has been explored in detail and the effect of the wireless channel on the network performance has been determined. The performance of some popular machine learning algorithms has been tested and compared with the neural network 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.