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

doi:10.1109/MWSCAS.2017.8053078

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 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 language	English (US)
Title of host publication	2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	934-937
Number of pages	4
ISBN (Electronic)	9781509063895
DOIs	https://doi.org/10.1109/MWSCAS.2017.8053078
State	Published - Sep 27 2017
Event	60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States Duration: Aug 6 2017 → Aug 9 2017

Publication series

Name	Midwest Symposium on Circuits and Systems
Volume	2017-August
ISSN (Print)	1548-3746

Other

Other	60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Country/Territory	United States
City	Boston
Period	8/6/17 → 8/9/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Publisher link

10.1109/MWSCAS.2017.8053078

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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). Article 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

Predicting hard and soft-responses and identifying stable challenges of MUX PUFs using ANNs. / Avvaru, S. V.Sandeep; Zhou, Chen; Kim, Chris H. et al.
2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 934-937 8053078 (Midwest Symposium on Circuits and Systems; Vol. 2017-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Avvaru, SVS, Zhou, C, Kim, CH & Parhi, KK 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., 8053078, Midwest Symposium on Circuits and Systems, vol. 2017-August, Institute of Electrical and Electronics Engineers Inc., pp. 934-937, 60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017, Boston, United States, 8/6/17. https://doi.org/10.1109/MWSCAS.2017.8053078

Avvaru SVS, Zhou C, Kim CH , Parhi KK. 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. Institute of Electrical and Electronics Engineers Inc. 2017. p. 934-937. 8053078. (Midwest Symposium on Circuits and Systems). doi: 10.1109/MWSCAS.2017.8053078

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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.",

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

Abstract

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