It is known that power-up values of embedded SRAM memory are unique for each individual chip. The uniqueness enables the power-up values to be considered as SRAM fingerprints used to verify device identities, which is a fundamental task in security applications. However, as the SRAM fingerprints are sensitive to environmental changes, there always exists a chance of error during the authentication process. Hence, the accuracy of a device authentication system with the SRAM fingerprints should be carefully estimated and verified in order to be implemented in practice. Consequently, a proper system evaluation method for the SRAM-based device authentication system should be provided. In this paper, we introduce tractable and computationally efficient system evaluation methods, which include novel parametric models for the distributions of matching distances among genuine and imposter devices. In addition, novel algorithms to calculate the confidence intervals of the estimates, which are crucial in system evaluation, are presented. Also, empirical results follow to validate the models and methods.
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