Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference

Anoop Koyily; Chen Zhou; Chris H. Kim; Keshab K. Parhi

doi:10.1109/ISCAS.2018.8351487

Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference

Anoop Koyily, Chen Zhou, Chris H. Kim, Keshab K. Parhi

Electrical and Computer Engineering

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

3 Scopus citations

Abstract

This paper presents a logistic regression based approach to predict the soft-response for a challenge using the total delay-difference as an input. This approach enables us to determine whether a challenge is stable or not. Soft-response is the probability of response bit corresponding to the challenge being 1. The total delay-difference is computed from the input challenge by assuming that the delay-difference of the stages are known. The approach learns a logistic function based on the total delay-difference which has just 3 parameters. Therefore, this is a simple approach which gives comparable performance against a more complex approach based on artificial neural network (ANN) models. The model demonstrates good sensitivity and precision but poor specificity. Furthermore, we use scaling parameter of the logistic function to study its relation to the arbiter's timing parameters like setup and hold time.

Original language	English (US)
Title of host publication	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538648810
DOIs	https://doi.org/10.1109/ISCAS.2018.8351487
State	Published - Apr 26 2018
Event	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy Duration: May 27 2018 → May 30 2018

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2018-May
ISSN (Print)	0271-4310

Other

Other	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Country/Territory	Italy
City	Florence
Period	5/27/18 → 5/30/18

Bibliographical note

Funding Information:
This research has been supported by the National Science Foundation under grant number CNS-1441639 and the semiconductor research corporation under contract number 2014-TS-2560.

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

MUX PUF
logistic regression
metastability
physical unclonable function
soft-response
stable challenges

Publisher link

10.1109/ISCAS.2018.8351487

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Koyily, A., Zhou, C., Kim, C. H., & Parhi, K. K. (2018). Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference. In 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings [8351487] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2018-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2018.8351487

Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference. / Koyily, Anoop; Zhou, Chen; Kim, Chris H. et al.
2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8351487 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2018-May).

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

Koyily, A, Zhou, C, Kim, CH & Parhi, KK 2018, Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference. in 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings., 8351487, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2018-May, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018, Florence, Italy, 5/27/18. https://doi.org/10.1109/ISCAS.2018.8351487

Koyily A, Zhou C, Kim CH , Parhi KK. Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference. In 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8351487. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS.2018.8351487

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abstract = "This paper presents a logistic regression based approach to predict the soft-response for a challenge using the total delay-difference as an input. This approach enables us to determine whether a challenge is stable or not. Soft-response is the probability of response bit corresponding to the challenge being 1. The total delay-difference is computed from the input challenge by assuming that the delay-difference of the stages are known. The approach learns a logistic function based on the total delay-difference which has just 3 parameters. Therefore, this is a simple approach which gives comparable performance against a more complex approach based on artificial neural network (ANN) models. The model demonstrates good sensitivity and precision but poor specificity. Furthermore, we use scaling parameter of the logistic function to study its relation to the arbiter's timing parameters like setup and hold time.",

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author = "Anoop Koyily and Chen Zhou and Kim, {Chris H.} and Parhi, {Keshab K.}",

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N1 - Funding Information: This research has been supported by the National Science Foundation under grant number CNS-1441639 and the semiconductor research corporation under contract number 2014-TS-2560. Publisher Copyright: © 2018 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

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N2 - This paper presents a logistic regression based approach to predict the soft-response for a challenge using the total delay-difference as an input. This approach enables us to determine whether a challenge is stable or not. Soft-response is the probability of response bit corresponding to the challenge being 1. The total delay-difference is computed from the input challenge by assuming that the delay-difference of the stages are known. The approach learns a logistic function based on the total delay-difference which has just 3 parameters. Therefore, this is a simple approach which gives comparable performance against a more complex approach based on artificial neural network (ANN) models. The model demonstrates good sensitivity and precision but poor specificity. Furthermore, we use scaling parameter of the logistic function to study its relation to the arbiter's timing parameters like setup and hold time.

AB - This paper presents a logistic regression based approach to predict the soft-response for a challenge using the total delay-difference as an input. This approach enables us to determine whether a challenge is stable or not. Soft-response is the probability of response bit corresponding to the challenge being 1. The total delay-difference is computed from the input challenge by assuming that the delay-difference of the stages are known. The approach learns a logistic function based on the total delay-difference which has just 3 parameters. Therefore, this is a simple approach which gives comparable performance against a more complex approach based on artificial neural network (ANN) models. The model demonstrates good sensitivity and precision but poor specificity. Furthermore, we use scaling parameter of the logistic function to study its relation to the arbiter's timing parameters like setup and hold time.

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Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference

Abstract

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