Latent Space Modeling for Cloning Encrypted PUF-Based Authentication

Abstract

Physically Unclonable Functions (PUFs) have emerged as a lightweight, viable security protocol in the Internet of Things (IoT) framework. While there have been recent works on crypt-analysis of PUF-based models, they require physical access to the device and knowledge of the underlying architecture along with unlimited access to the challenge-response pairs in plain text without encryption. In this work, we are the first to tackle the problem of encrypted PUF-based authentication in an IoT framework. We propose a novel, generative framework based on variational autoencoders that is PUF architecture-independent and can handle encryption protocols on the transmitted CRPs. We show that the proposed framework can successfully clone three (3) different PUF architectures encrypted using two (2) different encryption protocols in DES and AES. We also show that the proposed approach outperforms a brute-force machine learning-based attack model by over \(20\%\).