A Logic Encryption-Enhanced PUF Architecture to Deceive Machine Learning-Based Modeling Attacks

As a low-cost hardware security primitive, physically unclonable function (PDF) has been widely utilized in secure key generation and identity authentication of physical devices due to the advantages of high reliability and randomness. However, hackers can model a PDF circuit by collecting a small number of challenge- response pairs (CRPs), making it potentially vulnerable to machine learning (ML) attacks. To effectively resist this risk, various ML-resistance methods have been proposed. However, most of them mainly enhance the anti-attack ability of PDFs by structure nonlinear and CRP obfuscation, reducing stability and reliability. Therefore, we present a logic encryption-enhanced PDF (LEE PDF) architecture to resist ML-based attacks without affecting PDF performance. A logic encryption unit is applied to protect the function of original PDF circuits, thus concealing the valid CRPs in a large number of useless ones. Since hackers can only obtain a sparse number of useful CRPs without knowing the correct key, making it impossible to model PDF using ML methods. We have implemented the proposed LEE PDF on FPGA micro-boards. The experimental results demonstrate that the LEE PDF with only 2-bit key can effectively resist various ML- based attacks, average prediction rate is close to 50 %. In addition, the PDF performance remains almost constant.