A Modelling Attack Resistant Low Overhead Memristive Physical Unclonable Function - Yang, Xiaohan; Khandelwal, Saurabh; Jiang, Aiqi; Jabir, Abusaleh (2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), 2020)
Memristors are finding applications in memory, logic, neuromorphic systems, and data security. To this end, we leverage the non-linear behaviour of memristors to devise a low overhead physical unclonable function using a memristive chaos circuit in conjunction with a non-linear memristive encoder. We demonstrate the effectiveness of this architecture in Challenge-Response-Pair based authentication, and for its physical uncloneability. This architecture is highly versatile and can be implemented with a single encoder or a number of encoders running in parallel, each one with its own merit, for extending the sizes of CRPs. To demonstrate its effectiveness, we subject the architecture to machine learning based modelling attacks e.g. Logistic Regression, Support Vector Machines, Random Forest, as well as Artificial Neural Network classifiers. We found out that the proposed PUF architecture provides better resistance to such attacks, even for smaller bit sizes and at reduced overheads.