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As PUFs become ubiquitous for commercial products (e.g., FPGAs from Xilinx, Altera, and Microsemi), attacks against these primitives are evolving toward more omnipresent and even advanced techniques. Machine learning (ML) attacks, among other non-invasive attacks, are proven to be feasible and cost-effective in the real-world. However, for PUF designers, it still remains an open question whether their countermeasures, or even new designs, are resistant to these types of attacks. Although standard metrics for estimating PUF quality exist, the most common approaches for measuring resistance to ML attacks are empirical. This paper introduces PUFmeter, a new publicly available toolbox consisting of in-house developed algorithms, to provide a firm basis for the robustness assessment of PUFs against ML attacks. To this end, new metrics and notions are reintroduced by PUFmeter to PUF designers and manufacturers. Furthermore, to prepare the PUF input-output pairs adequately before conducting any analysis, PUFmeter involves modules that output the minimum number of measurement repetitions and the upper bound on the noise level affecting the PUF responses.

PUFmeter a Property Testing Tool for Assessing the Robustness of Physically Unclonable Functions to Machine Learning Attacks