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

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

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

2 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 languageEnglish (US)
Title of host publication2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538648810
DOIs
StatePublished - Apr 26 2018
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: May 27 2018May 30 2018

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2018-May
ISSN (Print)0271-4310

Other

Other2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Country/TerritoryItaly
CityFlorence
Period5/27/185/30/18

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

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