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Abstract
The physical unclonable function (PUF) is regarded as the root of trust of hardware systems. However, it suffers from the modeling attacks based on machine learning (ML) algorithms. Subsequently, the anti-modeling-attack PUF is of great concern from academia and industries in recent years. In practice, the security of a given PUF is evaluated after the pertinent attacks. However, these evaluation methods are not helpful for the PUF design, because the relationship between the PUF structure and the anti-modeling-attack capability is not established explicitly. This work proposes a security evaluation method of PUF based on the mimic attack and the Probably Approximately Correct (PAC) theory. The anti-modeling-attack capability of PUF is measured by the corresponding area enclosed by the evaluation curve. Twenty representative types of PUFs with different sizes are evaluated by the proposed method. It shows that the proposed method is effective, because the evaluation results are consistent with the difficulties of modeling attacks for the corresponding PUFs in the design practices. And the evaluation results are able to assist the PUF design.
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