Predicting hard and soft-responses and identifying stable challenges of MUX PUFs using ANNs

S. V.Sandeep Avvaru, Chen Zhou, Chris H. Kim, Keshab K. Parhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

In this paper, we present artificial neural network (ANN) models to predict hard and soft-responses of three configurations of arbiter based physical unclonable functions (PUFs): standard, feed-forward (FF) and modified feed-forward (MFF). The models are trained using data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process. The contributions of this paper are two-fold. First, we evaluate the unpredictability of the PUFs by predicting hard responses using ANNs and comparing these with ground truth. Second, ANNs are trained to predict soft-responses and a probability based thresholding scheme is used to define stability. The obtained soft-response models are used to identify unstable responses.

Original languageEnglish (US)
Title of host publication2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages934-937
Number of pages4
ISBN (Electronic)9781509063895
DOIs
StatePublished - Sep 27 2017
Event60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States
Duration: Aug 6 2017Aug 9 2017

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2017-August
ISSN (Print)1548-3746

Other

Other60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
CountryUnited States
CityBoston
Period8/6/178/9/17

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    Avvaru, S. V. S., Zhou, C., Kim, C. H., & Parhi, K. K. (2017). Predicting hard and soft-responses and identifying stable challenges of MUX PUFs using ANNs. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017 (pp. 934-937). [8053078] (Midwest Symposium on Circuits and Systems; Vol. 2017-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MWSCAS.2017.8053078