Open AccessThe Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks
Author(s) -
Phuong Ha Nguyen,
Durga Prasad Sahoo,
Chenglu Jin,
Kaleel Mahmood,
Ulrich Rührmair,
Marten van Dijk
Publication year - 2019
Publication title -
iacr transactions on cryptographic hardware and embedded systems
Language(s) - English
Resource type - Journals
ISSN - 2569-2925
DOI - 10.46586/tches.v2019.i4.243-290
Subject(s) - exploit , reliability (semiconductor) , physical unclonable function , computer science , implementation , field programmable gate array , embedded system , argument (complex analysis) , state (computer science) , stability (learning theory) , computer security , reliability engineering , cryptography , engineering , machine learning , software engineering , algorithm , power (physics) , biochemistry , physics , chemistry , quantum mechanics
The design of a silicon Strong Physical Unclonable Function (PUF) that is lightweight and stable, and which possesses a rigorous security argument, has been a fundamental problem in PUF research since its very beginnings in 2002. Various effective PUF modeling attacks, for example at CCS 2010 and CHES 2015, have shown that currently, no existing silicon PUF design can meet these requirements. In this paper, we introduce the novel Interpose PUF (iPUF) design, and rigorously prove its security against all known machine learning (ML) attacks, including any currently known reliability-based strategies that exploit the stability of single CRPs (we are the first to provide a detailed analysis of when the reliability based CMA-ES attack is successful and when it is not applicable). Furthermore, we provide simulations and confirm these in experiments with FPGA implementations of the iPUF, demonstrating its practicality. Our new iPUF architecture so solves the currently open problem of constructing practical, silicon Strong PUFs that are secure against state-of-the-art ML attacks.