  Traditional authentication in radio-frequency (RF) systems enable secure data
communication within a network through techniques such as digital signatures
and hash-based message authentication codes (HMAC), which suffer from key
recovery attacks. State-of-the-art IoT networks such as Nest also use Open
Authentication (OAuth 2.0) protocols that are vulnerable to cross-site-recovery
forgery (CSRF), which shows that these techniques may not prevent an adversary
from copying or modeling the secret IDs or encryption keys using invasive, side
channel, learning or software attacks. Physical unclonable functions (PUF), on
the other hand, can exploit manufacturing process variations to uniquely
identify silicon chips which makes a PUF-based system extremely robust and
secure at low cost, as it is practically impossible to replicate the same
silicon characteristics across dies. Taking inspiration from human
communication, which utilizes inherent variations in the voice signatures to
identify a certain speaker, we present RF- PUF: a deep neural network-based
framework that allows real-time authentication of wireless nodes, using the
effects of inherent process variation on RF properties of the wireless
transmitters (Tx), detected through in-situ machine learning at the receiver
(Rx) end. The proposed method utilizes the already-existing asymmetric RF
communication framework and does not require any additional circuitry for PUF
generation or feature extraction. Simulation results involving the process
variations in a standard 65 nm technology node, and features such as LO offset
and I-Q imbalance detected with a neural network having 50 neurons in the
hidden layer indicate that the framework can distinguish up to 4800
transmitters with an accuracy of 99.9% (~ 99% for 10,000 transmitters) under
varying channel conditions, and without the need for traditional preambles.
