RF-PUF: Enhancing IoT Security through Authentication of Wireless Nodes using In-situ Machine Learning
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Traditional authentication in radio-frequency (RF) systems enable secure data transmission within a network through techniques such as digital signatures and hash-based message authentication codes (HMAC). However, these techniques may not prevent a malicious attacker from stealing the secret encryption keys using invasive, modeling or side channel attacks. Physically 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. In this paper, 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 varying channel conditions, and without the need for traditional preambles.
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