Association for Computing Machinery
Physical Unclonable Functions (PUFs) have established themselves in the scientific literature, and are also gaining ground in commercial applications. Recently, however, several attacks on PUF core properties have been reported. They concern their physical and digital unclonability, as well as their assumed resilience against invasive or side channel attacks. In this paper, the authors join some of these techniques in order to further improve their effectiveness. The combination of machine-learning based modeling techniques with side channel information allows them to attack so-called XOR Arbiter PUFs and lightweight PUFs up to a size and complexity that was previously out of reach.