Predictive Analysis of 3D ReRAM-Based PUF for Securing the Internet of Things

In recent years, an explosion of IoT devices and its use leads threats to the privacy and security concerns of individual users and merchandises. As one of promising solutions, physical unclonable function (PDF) has been extensively studied. This paper investigates quality of randomness in the first generation of 3D analog ReRAM PDF primitives using measured and gathered data from fabricated ReRAM crossbars. This study is significant as the randomness quality of a PDF directly relates to its resilience against various model-building attacks, including machine learning attack. Experimental results verify near perfect (50%) predictability. It confirms the PDFs potentials for large-scale, yet small and power efficient, implementation of hardware intrinsic security primitives.