CRC-PUF: A Machine Learning Attack Resistant Lightweight PUF Construction
Elena Dubrova, Oscar Naslund, Bernhard Degen, Anders Gawell, Yang Yu · 2019
Adversarial machine learning is an emerging threat to security of Machine Learning (ML)-based systems. However, we can potentially use it as a weapon against ML-based attacks. In this paper, we focus on protecting Physical Unclonable Functions (PUFs) against ML-based modeling attacks. PUFs are an important cryptographic primitive for secret key generation and challenge-response authentication. However, none of the existing PUF constructions are both ML attack resistant and sufficiently lightweight to fit low-end embedded devices. We present a lightweight PUF construction, CRC-PUF, in which input challenges are de-synchronized from output responses to make a PUF model difficult to learn. The de-synchronization is done by an input transformation based on a Cyclic Redundancy Check (CRC). By changing the CRC generator polynomial for each new response, we assure that success probability of recovering the transformed challenge is at most 2-86 for 128-bit challenges and responses.