RF-PUF: IoT Security Enhancement through Authentication of Wireless Nodes using In-situ Machine Learning
This addresses security for IoT sensor networks by providing a cost-effective authentication method without extra hardware, though it is incremental as it builds on existing PUF concepts.
The paper tackles IoT security by authenticating wireless nodes using inherent RF hardware variations as a physical unclonable function, achieving a probability of false detection < 10e-3 for distinguishing up to 10000 transmitters under varying channel conditions.
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