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.
|Title of host publication
|2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2019
|14th IEEE Asia Pacific Conference on Circuits and Systems 2018 - Shangri-La Hotel, Chengdu, China
Duration: 26 Oct 2018 → 30 Oct 2018
|14th IEEE Asia Pacific Conference on Circuits and Systems 2018
|26/10/2018 → 30/10/2018
- Machine Learning
- Modeling Attacks
- Physical Unclonable Functions
ASJC Scopus subject areas
- Biomedical Engineering
- Electrical and Electronic Engineering