Abstract
Physically unclonable functions (PUFs) are emerging as hardware primitives for key-generation and light-weight authentication. Strong PUFs represent a variant of PUFs which respond to a user challenge with a response determined by its unique manufacturing process variations. Unfortunately many of the Strong PUFs have been shown to be vulnerable to model building attacks when an attacker has access to challenge and response pairs. In mounting a model building attack, typically machine learning is used to build a software model to forge the PUF. Researchers have long been interested in designing Strong PUFs that are resistant to model building attacks. However, with innovations in application of machine learning, nearly all Strong PUFs presented in the literature have been broken. In this paper, first we present results from a set of experiments designed to show that if certain randomness properties can be met, cascaded structure based Strong PUFs can indeed be made machine learning (ML) attack resistant against known ML attacks. Next we conduct machine learning experiments on an abstract PUF model using Support Vector Machines, Logistic Regression, Bagging, Boosting and Evolutionary techniques to establish criteria for machine learning resistant Strong PUF design. This paper does not suggest how to harvest the process variation, which remains within the purview of a circuit designer; rather it suggests what properties of the building blocks to aim for towards building a machine learning resistant Strong PUF — thus paving the path for a systematic design approach.