Attacking Arbiter PUFs Using Various Modeling Attack Algorithms: A Comparative Study

Yue Fang, Qingqing Ma, Chongyan Gu, Chenghua Wang, Maire O'Neill, Weiqiang Liu

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

1 Citation (Scopus)
173 Downloads (Pure)

Abstract

In this paper, we investigate the effectiveness of four different modeling attack algorithms, including Logistic Regression (LR), Naïve Bayes, AdaBoost and Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES), on attacking arbiter physical unclonable functions (APUFs). A comparison of experimental results using theses algorithms is presented. The results show that the performance of the algorithms is related to the number of training data, the noise level involved in the APUF design and the number of stages in the generation of each bit response. It is found that the mainstream LR and CMA-ES are worse for a small number of data compared with Naïve Bayes and AdaBoost.
Original languageEnglish
Title of host publication2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)
Publisher IEEE
Pages394-397
Number of pages4
ISBN (Electronic)978-1-5386-8240-1
DOIs
Publication statusPublished - 2019
Event14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018 - Chengdu, China
Duration: 26 Oct 201830 Oct 2018

Conference

Conference14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018
CountryChina
CityChengdu
Period26/10/201830/10/2018

Keywords

  • Machine Learning
  • Modeling Attacks
  • Physical Unclonable Functions

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Instrumentation

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  • Cite this

    Fang, Y., Ma, Q., Gu, C., Wang, C., O'Neill, M., & Liu, W. (2019). Attacking Arbiter PUFs Using Various Modeling Attack Algorithms: A Comparative Study. In 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) (pp. 394-397). [8605618] IEEE . https://doi.org/10.1109/APCCAS.2018.8605618, https://doi.org/10.1109/APCCAS.2018.8605618