Modeling Strong Physically Unclonable Functions with Metaheuristics

TL;DR

This paper systematically evaluates various metaheuristics for attacking strong PUFs using challenge-response pairs, confirming CMA-ES as the most effective but highlighting alternatives with lower computational costs.

Contribution

It provides a comprehensive comparison of metaheuristics for PUF attacks, identifying alternatives to CMA-ES with comparable performance and lower computational costs.

Findings

CMA-ES performs best among tested metaheuristics.

Several algorithms show similar performance with less computational cost.

Training with sufficient challenge-response pairs enhances attack success.

Abstract

Evolutionary algorithms have been successfully applied to attacking Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most powerful option for a type of attack called the reliability attack. While there is no reason to doubt the performance of CMA-ES, the lack of comparison with different metaheuristics and results for the challenge-response pair-based attack leaves open questions if there are better-suited metaheuristics for the problem. In this paper, we take a step back and systematically evaluate several metaheuristics for the challenge-response pair-based attack on strong PUFs. Our results confirm that CMA-ES has the best performance, but we also note several other algorithms with similar performance while having smaller computational costs. More precisely, if we provide a sufficient number of challenge-response pairs to train the algorithm, various configurations show good results. Consequently, we conclude that EAs represent a strong option for challenge-response pair-based attacks on PUFs.