Modeling Strong Physically Unclonable Functions with Metaheuristics
This work addresses the security of PUFs by comparing attack methods, but it is incremental as it builds on existing evolutionary algorithm approaches.
The paper systematically evaluated multiple metaheuristics for challenge-response pair-based attacks on strong Physically Unclonable Functions (PUFs), confirming that CMA-ES has the best performance but noting other algorithms with similar results and lower computational costs.
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.