Building lightweight security for low-cost pervasivedevices is a major challenge considering the design requirementsof a small footprint and low power consumption. Physical UnclonableFunctions (PUFs) have emerged as a promising technology toprovide a low-cost authentication for such devices. By exploitingintrinsic manufacturing process variations, PUFs are able togenerate unique and apparently random chip identifiers. Strong-PUFs represent a variant of PUFs that have been suggestedfor lightweight authentication applications. Unfortunately, manyof the Strong-PUFs have been shown to be susceptible tomodelling attacks (i.e., using machine learning techniques) inwhich an adversary has access to challenge and response pairs.In this study, we propose an obfuscation technique during postprocessingof Strong-PUF responses to increase the resilienceagainst machine learning attacks. We conduct machine learningexperiments using Support Vector Machines and Artificial NeuralNetworks on two Strong-PUFs: a 32-bit Arbiter-PUF and a 2-XOR 32-bit Arbiter-PUF. The predictability of the 32-bit Arbiter-PUF is reduced to ~ 70% by using an obfuscation technique.Combining the obfuscation technique with 2-XOR 32-bit Arbiter-PUF helps to reduce the predictability to ~ 64%. More reductionin predictability has been observed in an XOR Arbiter-PUFbecause this PUF architecture has a good uniformity. The areaoverhead with an obfuscation technique consumes only 788 and1080 gate equivalents for the 32-bit Arbiter-PUF and 2-XOR 32-bit Arbiter-PUF, respectively.
展开▼
