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Due to the diverse and mobile nature of the deployment environment, smartcommodity devices are vulnerable to various attacks which can grantunauthorized access to a rogue device in a large, connected network.Traditional digital signature-based authentication methods are vulnerable tokey recovery attacks, CSRF, etc. To circumvent this, RF-PUF had been proposedas a promising alternative that utilizes the inherent nonidealities of thedevices as physical signatures. RF-PUF offers a robust authentication methodthat is resilient to key-hacking methods due to the absence of secret keyrequirements and does not require any additional circuitry on the transmitterend, eliminating additional power, area, and computational burden. In thiswork, for the first time, we analyze the effectiveness of RF-PUF on commoditydevices, purchased off-the-shelf, without any modifications whatsoever. Datawere collected from 30 Xbee S2C modules and released as a public dataset. A newfeature has been engineered through statistical property analysis. With a newand robust feature set, it has been shown that 95% accuracy can be achievedusing only ~1.8 ms of test data, reaching >99.8% accuracy with more data and anetwork of higher model capacity, without any assisting digital preamble. Thedesign space has been explored in detail and the effect of the wireless channelhas been determined. The performance of some popular ML algorithms has beencompared with the NN approach. A thorough investigation on various PUFproperties has been done and both intra and inter-PUF distances have beencalculated. With extensive testing of 41238000 cases, the detection probabilityfor 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 beendiscussed.

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