Title :
A 1-bit Physically Unclonable Function based on a two-neurons CNN
Author :
Addabbo, Tommaso ; Fort, Ada ; Di Marco, Mauro ; Pancioni, Luca ; Vignoli, Valerio
Author_Institution :
Inf. Eng. Dept., Univ. of Siena, Siena, Italy
Abstract :
We propose to exploit a two-neurons Cellular Neural Network (CNN) to design a basic 1-bit Physically Unclonable Function (PUF). The analysis discussed in this work, derived from the general theory of CNNs, has been validated by experimental results.
Keywords :
cellular neural nets; physically unclonable function; two-neurons CNN; two-neurons cellular neural network; Chaos; Cryptography; Integrated circuit modeling; Mathematical model; Neurons; Radio frequency; Robustness;
Conference_Titel :
Circuits and Systems (ISCAS), 2013 IEEE International Symposium on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-5760-9
DOI :
10.1109/ISCAS.2013.6572393
