Countering PUF Modeling Attacks through Adversarial Machine Learning

Ebrahimabadi, Mohammad et al.; Countering PUF Modeling Attacks through Adversarial Machine Learning; IEEE Computer Society Annual Symposium on VLSI (ISVLSI), July 2021;

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Abstract

A Physically Unclonable Function (PUF) is an effective option for device authentication, especially for IoT frameworks with resource-constrained devices. However, PUFs are vulnerable to modeling attacks which build a PUF model using a small subset of its Challenge-Response Pairs (CRPs). We propose an effective countermeasure against such an attack by employing adversarial machine learning techniques that introduce errors (poison) to the adversary’s model. The approach intermittently provides wrong response for the fed challenges. Coordination among the communicating parties is pursued to prevent the poisoned CRPs from causing the device authentication to fail. The experimental results extracted for a PUF implemented on FPGA demonstrate the efficacy of the proposed approach in thwarting modeling attacks. We also discuss the resiliency of the proposed scheme against impersonation and Sybil attacks.

Countering PUF Modeling Attacks through Adversarial Machine Learning

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