IEICE TRANSACTIONS on Fundamentals
BayesianPUFNet: Training Sample Efficient Modeling Attack for Physically Unclonable Functions
Summary :
This paper proposes a deep neural network named BayesianPUFNet that can achieve high prediction accuracy even with few challenge-response pairs (CRPs) available for training. Generally, modeling attacks are a vulnerability that could compromise the authenticity of physically unclonable functions (PUFs); thus, various machine learning methods including deep neural networks have been proposed to assess the vulnerability of PUFs. However, conventional modeling attacks have not considered the cost of CRP collection and analyzed attacks based on the assumption that sufficient CRPs were available for training; therefore, previous studies may have underestimated the vulnerability of PUFs. Herein, we show that the application of Bayesian deep neural networks that incorporate Bayesian statistics can provide accurate response prediction even in situations where sufficient CRPs are not available for learning. Numerical experiments show that the proposed model uses only half the CRP to achieve the same response prediction as that of the conventional methods. Our code is openly available on https://github.com/bayesian-puf-net/bayesian-puf-net.git.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.5 pp.840-850
- Publication Date
- 2023/05/01
- Publicized
- 2022/10/31
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022EAP1061
- Type of Manuscript
- PAPER
- Category
- Cryptography and Information Security
Authors
Hiromitsu AWANO
Kyoto University
Makoto IKEDA
The University of Tokyo
Keyword
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Hiromitsu AWANO, Makoto IKEDA, "BayesianPUFNet: Training Sample Efficient Modeling Attack for Physically Unclonable Functions" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 5, pp. 840-850, May 2023, doi: 10.1587/transfun.2022EAP1061.
Abstract: This paper proposes a deep neural network named BayesianPUFNet that can achieve high prediction accuracy even with few challenge-response pairs (CRPs) available for training. Generally, modeling attacks are a vulnerability that could compromise the authenticity of physically unclonable functions (PUFs); thus, various machine learning methods including deep neural networks have been proposed to assess the vulnerability of PUFs. However, conventional modeling attacks have not considered the cost of CRP collection and analyzed attacks based on the assumption that sufficient CRPs were available for training; therefore, previous studies may have underestimated the vulnerability of PUFs. Herein, we show that the application of Bayesian deep neural networks that incorporate Bayesian statistics can provide accurate response prediction even in situations where sufficient CRPs are not available for learning. Numerical experiments show that the proposed model uses only half the CRP to achieve the same response prediction as that of the conventional methods. Our code is openly available on https://github.com/bayesian-puf-net/bayesian-puf-net.git.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1061/_p
Copy
@ARTICLE{e106-a_5_840,
author={Hiromitsu AWANO, Makoto IKEDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={BayesianPUFNet: Training Sample Efficient Modeling Attack for Physically Unclonable Functions},
year={2023},
volume={E106-A},
number={5},
pages={840-850},
abstract={This paper proposes a deep neural network named BayesianPUFNet that can achieve high prediction accuracy even with few challenge-response pairs (CRPs) available for training. Generally, modeling attacks are a vulnerability that could compromise the authenticity of physically unclonable functions (PUFs); thus, various machine learning methods including deep neural networks have been proposed to assess the vulnerability of PUFs. However, conventional modeling attacks have not considered the cost of CRP collection and analyzed attacks based on the assumption that sufficient CRPs were available for training; therefore, previous studies may have underestimated the vulnerability of PUFs. Herein, we show that the application of Bayesian deep neural networks that incorporate Bayesian statistics can provide accurate response prediction even in situations where sufficient CRPs are not available for learning. Numerical experiments show that the proposed model uses only half the CRP to achieve the same response prediction as that of the conventional methods. Our code is openly available on https://github.com/bayesian-puf-net/bayesian-puf-net.git.},
keywords={},
doi={10.1587/transfun.2022EAP1061},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - BayesianPUFNet: Training Sample Efficient Modeling Attack for Physically Unclonable Functions
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 840
EP - 850
AU - Hiromitsu AWANO
AU - Makoto IKEDA
PY - 2023
DO - 10.1587/transfun.2022EAP1061
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2023
AB - This paper proposes a deep neural network named BayesianPUFNet that can achieve high prediction accuracy even with few challenge-response pairs (CRPs) available for training. Generally, modeling attacks are a vulnerability that could compromise the authenticity of physically unclonable functions (PUFs); thus, various machine learning methods including deep neural networks have been proposed to assess the vulnerability of PUFs. However, conventional modeling attacks have not considered the cost of CRP collection and analyzed attacks based on the assumption that sufficient CRPs were available for training; therefore, previous studies may have underestimated the vulnerability of PUFs. Herein, we show that the application of Bayesian deep neural networks that incorporate Bayesian statistics can provide accurate response prediction even in situations where sufficient CRPs are not available for learning. Numerical experiments show that the proposed model uses only half the CRP to achieve the same response prediction as that of the conventional methods. Our code is openly available on https://github.com/bayesian-puf-net/bayesian-puf-net.git.
ER -
English
