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

TL;DR

This paper introduces RF-PUF, a wireless node authentication method using inherent RF property variations and deep learning, enhancing IoT security without extra hardware in transmitters.

Contribution

It presents a novel RF-PUF framework leveraging existing RF hardware and deep neural networks for robust transmitter identification in IoT networks.

Findings

Can distinguish up to 10,000 transmitters

Achieves false detection probability below 10^-3

Works under varying channel conditions

Abstract

Physical unclonable functions (PUF) in silicon exploit die-to-die manufacturing variations during fabrication for uniquely identifying each die. Since it is practically a hard problem to recreate exact silicon features across dies, a PUFbased authentication system is robust, secure and cost-effective, as long as bias removal and error correction are taken into account. In this work, we utilize the effects of inherent process variation on analog and radio-frequency (RF) properties of multiple wireless transmitters (Tx) in a sensor network, and detect the features at the receiver (Rx) using a deep neural network based framework. The proposed mechanism/framework, called RF-PUF, harnesses already existing RF communication hardware and does not require any additional PUF-generation circuitry in the Tx for practical implementation. Simulation results indicate that the RF-PUF framework can distinguish up to 10000 transmitters (with standard foundry defined variations for a 65 nm process, leading to non-idealities such as LO offset and I-Q imbalance) under varying channel conditions, with a probability of false detection < 10e-3