Defeating Strong PUF Modeling Attack via Adverse Selection of Challenge-Response Pairs
Horácio L. França, Charles Bezerra do Prado, Vinay C. Patil, Sandip Kumar Kundu · 2018
Most advances in increasing the security of PUFs come from alterations to the topology of the mechanism or the measurement used to generate their output. This paper focuses on a different form of improving security: Selecting a subset of challenges and responses to conceal the patterns inherent in a PUF well enough to prevent an attacker from successfully replicating the PUF's responses. Our results show that it is possible to select a large set of CRPs that can be exposed to an attacker resulting in a modeling accuracy as low as 74%, while without our selection process the accuracy increases to 93%.