Visible to the public Lightweight Obfuscation Techniques for Modeling Attacks Resistant PUFs

TitleLightweight Obfuscation Techniques for Modeling Attacks Resistant PUFs
Publication TypeConference Paper
Year of Publication2017
AuthorsMispan, M. S., Halak, B., Zwolinski, M.
Conference Name2017 IEEE 2nd International Verification and Security Workshop (IVSW)
Date Publishedjul
ISBN Number978-1-5386-1708-3
KeywordsArbiter-PUF, Artificial neural networks, authentication, delays, Human Behavior, human factors, learning (artificial intelligence), lightweight obfuscation, Logic gates, low-cost authentication, low-cost pervasive devices, machine learning, machine-learning, message authentication, Metrics, neural nets, physical unclonable function (PUF), physical unclonable functions, pubcrawl, PUF, resilience, Resiliency, Resistance, Scalability, Support vector machines, ubiquitous computing, Ubiquitous Computing Security

Building lightweight security for low-cost pervasive devices is a major challenge considering the design requirements of a small footprint and low power consumption. Physical Unclonable Functions (PUFs) have emerged as a promising technology to provide a low-cost authentication for such devices. By exploiting intrinsic manufacturing process variations, PUFs are able to generate unique and apparently random chip identifiers. Strong-PUFs represent a variant of PUFs that have been suggested for lightweight authentication applications. Unfortunately, many of the Strong-PUFs have been shown to be susceptible to modelling attacks (i.e., using machine learning techniques) in which an adversary has access to challenge and response pairs. In this study, we propose an obfuscation technique during post-processing of Strong-PUF responses to increase the resilience against machine learning attacks. We conduct machine learning experiments using Support Vector Machines and Artificial Neural Networks 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 $\approx$ 70% by using an obfuscation technique. Combining the obfuscation technique with 2-XOR 32-bit Arbiter-PUF helps to reduce the predictability to $\approx$ 64%. More reduction in predictability has been observed in an XOR Arbiter-PUF because this PUF architecture has a good uniformity. The area overhead with an obfuscation technique consumes only 788 and 1080 gate equivalents for the 32-bit Arbiter-PUF and 2-XOR 32-bit Arbiter-PUF, respectively.

Citation Keymispan_lightweight_2017