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Physically Unclonable Functions Derived From Cellular Neural Networks

2013; Institute of Electrical and Electronics Engineers; Volume: 60; Issue: 12 Linguagem: Inglês

10.1109/tcsi.2013.2255691

1558-0806

Tommaso Addabbo, Ada Fort, Mauro Di Marco, Luca Pancioni, Valerio Vignoli,

Neuroscience and Neural Engineering

We propose the design of Physically Unclonable Functions (PUFs) exploiting the nonlinear behavior of Cellular Neural Networks (CNNs). Our work derives from some theoretical results achieved within the theory of CNNs, adapted to a simpler case. The theoretical analysis discussed in this work has a general validity, whereas the presented basic hardware solution (i.e., the PUF electronic implementation) has to be understood as a reference demonstrating circuit to be further optimized and developed for a profitable use of the proposed approach. Theoretical results have been validated experimentally.