Design of a Delay-Based FPGA PUF Resistant to Machine Learning Attacks

Abstract

Physical unclonable functions (PUFs) are used to extract unique signatures from silicon-based chips which can be used for chip authentication and producing unclonable cryptographic keys. However, researchers have found that PUFs are vulnerable to various machine learning modeling attacks. In this work, we introduce a unique hybrid PUF structure that uses Challenge-Response Pairs (CRPs) from an Arbiter PUF and feeds them to an XOR-Inverter based Ring Oscillator to generate responses which makes the PUF less vulnerable to machine learning modeling attacks. From the results, it is found that the prediction accuracy when different machine learning classifier algorithms are employed to attack the PUF, is drastically reduced and lies in the range of 3.5% to 6.8%, whereas the ANN-based model accuracy obtained is in the range of 5.4% to 7.5%. Our study indicates that the new design’s vulnerability in terms of prediction accuracy against different machine learning modeling attacks is less by 51.6% for ML and 54.1% for ANN compared to other delay-based PUF designs.