Modeling Strong Physically Unclonable Functions with Metaheuristics

• URL: http://arxiv.org/abs/2202.08079v1
• Date: Wed, 16 Feb 2022 14:00:16 GMT
• Title: Modeling Strong Physically Unclonable Functions with Metaheuristics
• Authors: Carlos Coello Coello, Marko Djurasevic, Domagoj Jakobovic, Luca Mariot, Stjepan Picek
• Abstract summary: Evolutionary algorithms have been successfully applied to attacking Physically Unclonable Functions. CMA-ES is recognized as the most powerful option for a type of attack called the reliability attack. We take a step back and evaluate several metaheuristics for the challenge-response pair-based attack on strong PUFs.
• Score: 7.673465837624365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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