Hybrid Boolean Networks as Physically Unclonable Functions
Noeloikeau F. Charlot, Daniel Canaday, Andrew Pomerance, Daniel J. Gauthier · IEEE Access · 2021
We introduce a Physically Unclonable Function (PUF) based on an ultra-fast chaotic network known as a Hybrid Boolean Network (HBN) implemented on a field programmable gate array. The network, consisting of N coupled asynchronous logic gates displaying dynamics on the sub-nanosecond time scale, acts as a `digital fingerprint' by amplifying small manufacturing variations during a period of transient chaos. In contrast to other PUF designs, we use both N-bits per challenge and obtain N-bits per response by considering challenges to be initial states of the N-node network and responses to be states captured during the subsequent chaotic transient. We find that the presence of chaos amplifies the frozen-in randomness due to manufacturing differences and that the extractable entropy is approximately 50% of the maximum of N2Nbits. We obtain PUF uniqueness and reliability metrics μinter= 0.40±0.01 and μintra= 0.05±0.00, respectively, for an N=256 network. These metrics correspond to an expected Hamming distance of 102.4 bits per response. Moreover, a simple cherry-picking scheme that discards noisy bits yields μintra<; 0.01 while still retaining ~ 200 bits/response (corresponding to a Hamming distance of ~ 80 bits/response). In addition to characterizing the uniqueness and reliability, we demonstrate super-exponential scaling in the entropy up to N=512 and demonstrate that PUFmeter, a recent PUF analysis tool, is unable to model our PUF. Finally, we characterize the temperature variation of the HBN-PUF and propose future improvements.