Abstract:Traditional hardware security primitives such as physical unclonable functions (PUFs) are quite vulnerable to machine learning (ML) attacks. The primary reason is that PUFs rely on process mismatches between two identically designed circuit blocks to generate deterministic math functions as the secret information sources. Unfortunately, ML algorithms are pretty efficient in modeling deterministic math functions. In order to resist against ML attacks, in this letter, a novel hardware security primitive named ne… Show more
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