RF-PUF: Enhancing IoT Security through Authentication of Wireless Nodes using In-situ Machine Learning

TL;DR

RF-PUF leverages inherent RF signal variations and deep learning to authenticate wireless IoT devices in real-time, enhancing security without extra hardware or preambles.

Contribution

This work introduces a neural network-based RF-PUF framework that uses in-situ RF property variations for device authentication, eliminating the need for additional circuitry.

Findings

Achieves 99.9% accuracy in distinguishing up to 4800 transmitters.

Operates effectively without traditional preambles.

Maintains high accuracy (~99%) for 10,000 transmitters under varying conditions.

Abstract

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.