论文标题
用元启发术对强大的身体无统治的功能进行建模
Modeling Strong Physically Unclonable Functions with Metaheuristics
论文作者
论文摘要
进化算法已成功地应用于攻击物理上无统治的功能（PUF）。 CMA-Es被认为是称为可靠性攻击的攻击类型的最强大选择。尽管没有理由怀疑CMA-ES的性能，但与基于挑战反应对的攻击的不同元启发术和结果缺乏比较，如果有更好的元启发式化问题，则可以解决问题。 在本文中，我们退后一步，系统地评估了基于挑战反应对的强PUF的攻击攻击的几种元启发术。我们的结果证实了CMA-ES的性能最佳，但我们还注意到其他几种具有相似性能的算法，而计算成本较小。更确切地说，如果我们提供了足够数量的挑战 - 响应对来训练算法，则各种配置显示出良好的结果。因此，我们得出的结论是，EAS代表了基于挑战反应对的PUF攻击的强大选择。
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.
