PARLE-G: Provable Automated Representation and Analysis Framework for Learnability Evaluation of Generic PUF Compositions

Abstract

Besides enormous research efforts in the design of Physically Unclonable Functions (PUFs), its vulnerabilities are still being exploited using machine learning (ML) based model-building attacks. Due to inherent complicacy in exploring and manually converging to a strong PUF composition, the challenge of building ML-attack resistant PUFs continues. Hence, it becomes imperative to develop an automated framework that can formally assess the learnability of different PUF constructions and compositions to guide the designer to explore resilient PUFs. In this work, we present an automated analysis framework (PARLE-G), to formally represent and evaluate the Probably Approximately Correct (PAC) learnability of PUF constructions and their compositions. A high-level specification language PUF-G has been developed to structurally represent any PUF composition comprising a specified set of primitive components and composition operations. The tool takes a PUF design represented in PUF-G language as input and returns its PAC learnability result, identifying a suitable PAC learning algorithm and the PAC model parameters based on the input PUF design. PUF designs proven to be learnable by PARLE-G are segregated into different classes based on the asymptotic complexity of their learnability bounds. Such automated analysis helps a designer to make informed design choices, thereby strengthening a PUF construction from the architectural level.