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Physical Unclonable Functions (PUFs) are promising security primitives forresource-constrained network nodes. The XOR Arbiter PUF (XOR PUF or XPUF) is anintensively studied PUF invented to improve the security of the Arbiter PUF,probably the most lightweight delay-based PUF. Recently, highly powerfulmachine learning attack methods were discovered and were able to easily breaklarge-sized XPUFs, which were highly secure against earlier machine learningattack methods. Component-differentially-challenged XPUFs (CDC-XPUFs) are XPUFswith different component PUFs receiving different challenges. Studies showedthey were much more secure against machine learning attacks than theconventional XPUFs, whose component PUFs receive the same challenge. But thesestudies were all based on earlier machine learning attack methods, and hence itis not clear if CDC-XPUFs can remain secure under the recently discoveredpowerful attack methods. In this paper, the two current most powerful twomachine learning methods for attacking XPUFs are adapted by fine-tuning theparameters of the two methods for CDC-XPUFs. Attack experiments using bothsimulated PUF data and silicon data generated from PUFs implemented onfield-programmable gate array (FPGA) were carried out, and the experimentalresults showed that some previously secure CDC-XPUFs of certain circuitparameter values are no longer secure under the adapted new attack methods,while many more CDC-XPUFs of other circuit parameter values remain secure.Thus, our experimental attack study has re-defined the boundary between thesecure region and the insecure region of the PUF circuit parameter space,providing PUF manufacturers and IoT security application developers withvaluable information in choosing PUFs with secure parameter values.

A New Security Boundary of Component Differentially Challenged XOR PUFs Against Machine Learning Modeling Attacks