  Security is of critical importance for the Internet of Things (IoT). Many IoT
devices are resource-constrained, calling for lightweight security protocols.
Physical unclonable functions (PUFs) leverage integrated circuits' variations
to produce responses unique for individual devices, and hence are not
reproducible even by the manufacturers. Implementable with simplistic circuits
of thousands of transistors and operable with low energy, Physical unclonable
functions are promising candidates as security primitives for
resource-constrained IoT devices. Arbiter PUFs (APUFs) are a group of
delay-based PUFs which are highly lightweight in resource requirements but
suffer from high susceptibility to machine learning attacks. To defend APUF
variants against machine learning attacks, we introduce challenge input
interface, which incurs low resource overhead. With the interface, experimental
attack study shows that all tested PUFs have substantially improved their
resistance against machine learning attacks, rendering interfaced APUF variants
promising candidates for security critical applications.
