On the security of strong memristor-based physically unclonable functions

PUFs are cost-effective security primitives that extract unique identifiers from integrated circuits. However, since their introduction, PUFs have been subject to modeling attacks based on machine learning. Recently, researchers explored emerging nano-electronic technologies, e.g., memristors, to construct hybrid-PUFs, which outperform CMOS-only PUFs and are claimed to be more resilient to modeling attacks. However, since such PUF designs are not open-source, the security claims remain dubious. In this paper, we reproduce a set of memristor-PUFs and extensively evaluate their unpredictability property. By leveraging state-of-the-art machine learning algorithms, we show that it is feasible to successfully model memristor-PUFs with high prediction rates of 98%. Even incorporating XOR gates, to further strengthen PUFs' against modeling attacks, has a negligible effect.