Formal Analysis of Physically Unclonable Functions

In this research work, we aim to formalize the analysis of Physically Unclonable Functions (PUF) constructions. First, we present a testability analysis scheme that leverages the correlation spectra properties of Boolean functions to assess the quality of a collection of PUF instances of the same make by comparing its correlation spectra with that of a collection of known good PUF instances. Further, in the research, we propose a CAD framework that automatically assesses the learnability of a PUF construction in the PAC Learning model. To represent a PUF design, we propose a formal PUF representation language capable of representing any PUF construction or composition upfront. Next, we present a non-linearity assisted reliability based ML attack on a contemporary PUF construction, named Sn-PUF. We leverage the non-linearity of the Bent function to launch a reliability-based ML attack, that is able to break upto S12-PUF.