Skip to main content

2023 | OriginalPaper | Buchkapitel

6. Combinational Logic-Based Implementation of PUF

verfasst von : Pranesh Santikellur, Rajat Subhra Chakraborty

Erschienen in: Deep Learning for Computational Problems in Hardware Security

Verlag: Springer Nature Singapore

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In the earlier chapters, we have noted that a standalone arbiter PUF is vulnerable to machine learning (ML) attacks; however, multiple instances of arbiter PUF can be combined to create more robust PUF variants.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Choi, A., Shi, W., Shih, A., & Darwiche, A. (2019). Compiling neural networks into tractable boolean circuits. In AAAI Spring Symposium on Verification of Neural Networks (VNN). Choi, A., Shi, W., Shih, A., & Darwiche, A. (2019). Compiling neural networks into tractable boolean circuits. In AAAI Spring Symposium on Verification of Neural Networks (VNN).
2.
Zurück zum Zitat Gao, Y., Ma, H., Al-Sarawi, S. F., Abbott, D., & Ranasinghe, D. C. (2018). PUF-FSM: A controlled strong PUF. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(5), 1104–1108. Gao, Y., Ma, H., Al-Sarawi, S. F., Abbott, D., & Ranasinghe, D. C. (2018). PUF-FSM: A controlled strong PUF. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(5), 1104–1108.
3.
Zurück zum Zitat Hubara, I., Courbariaux, et al. (2016). Binarized neural networks. In Advances in Neural Information Processing Systems (pp. 4107–4115). Hubara, I., Courbariaux, et al. (2016). Binarized neural networks. In Advances in Neural Information Processing Systems (pp. 4107–4115).
4.
Zurück zum Zitat Majzoobi, M. (2012). Slender PUF protocol: A lightweight, robust, and secure authentication by substring matching. In IEEE Symposium on Security and Privacy Workshops (pp. 33–44). Majzoobi, M. (2012). Slender PUF protocol: A lightweight, robust, and secure authentication by substring matching. In IEEE Symposium on Security and Privacy Workshops (pp. 33–44).
6.
Zurück zum Zitat Ghasemzadeh, M., Samragh, M., & Koushanfar, F. (2018). ReBNet: Residual binarized neural network. In IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (pp. 57–64). Ghasemzadeh, M., Samragh, M., & Koushanfar, F. (2018). ReBNet: Residual binarized neural network. In IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (pp. 57–64).
8.
Zurück zum Zitat Riazi, M. S., Samragh, M., Chen, H., Laine, K., Lauter, K., & Koushanfar, F. (2019). XONN: XNOR-based oblivious deep neural network inference. In 28th USENIX Security Symposium (pp. 1501–1518). Riazi, M. S., Samragh, M., Chen, H., Laine, K., Lauter, K., & Koushanfar, F. (2019). XONN: XNOR-based oblivious deep neural network inference. In 28th USENIX Security Symposium (pp. 1501–1518).
9.
Zurück zum Zitat Yu, M.-D., Hiller, M., Delvaux, J., Sowell, R., Devadas, S., & Verbauwhede, I. (2016). A lockdown technique to prevent machine learning on PUFs for lightweight authentication. IEEE Transactions on Multi-Scale Computing Systems, 2(3), 146–159.CrossRef Yu, M.-D., Hiller, M., Delvaux, J., Sowell, R., Devadas, S., & Verbauwhede, I. (2016). A lockdown technique to prevent machine learning on PUFs for lightweight authentication. IEEE Transactions on Multi-Scale Computing Systems, 2(3), 146–159.CrossRef
10.
Zurück zum Zitat Shih, A., Darwiche, A., & Choi, A. (2019). Verifying binarized neural networks by angluin-style learning. In International Conference on Theory and Applications of Satisfiability Testing (pp. 354–370). Springer. Shih, A., Darwiche, A., & Choi, A. (2019). Verifying binarized neural networks by angluin-style learning. In International Conference on Theory and Applications of Satisfiability Testing (pp. 354–370). Springer.
11.
Zurück zum Zitat Shih, A., Darwiche, A., & Choi, A. (2019). Verifying binarized neural networks by local automaton learning. In AAAI Spring Symposium on Verification of Neural Networks (VNN). Shih, A., Darwiche, A., & Choi, A. (2019). Verifying binarized neural networks by local automaton learning. In AAAI Spring Symposium on Verification of Neural Networks (VNN).
12.
Zurück zum Zitat Chatterjee, U., Chakraborty, R. S., & Mukhopadhyay, D. (2017). A PUF-based secure communication protocol for IoT. ACM Transactions on Embedded Computing Systems, 16(3), 1–25.CrossRef Chatterjee, U., Chakraborty, R. S., & Mukhopadhyay, D. (2017). A PUF-based secure communication protocol for IoT. ACM Transactions on Embedded Computing Systems, 16(3), 1–25.CrossRef
13.
Zurück zum Zitat Chi, C.-C., & Jiang, J.-H. R. (2018). Logic synthesis of binarized neural networks for efficient circuit implementation. In IEEE/ACM International Conference on Computer-Aided Design (ICCAD) (pp. 1–7). Chi, C.-C., & Jiang, J.-H. R. (2018). Logic synthesis of binarized neural networks for efficient circuit implementation. In IEEE/ACM International Conference on Computer-Aided Design (ICCAD) (pp. 1–7).
15.
Zurück zum Zitat Delvaux, J., et al. (2015). Helper data algorithms for PUF-based key generation: Overview and analysis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 34(6), 889–902.CrossRef Delvaux, J., et al. (2015). Helper data algorithms for PUF-based key generation: Overview and analysis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 34(6), 889–902.CrossRef
16.
Zurück zum Zitat Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). XNOR-Net: ImageNet classification using binary convolutional neural networks. In European Conference on Computer Vision (pp. 525–542). Springer. Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). XNOR-Net: ImageNet classification using binary convolutional neural networks. In European Conference on Computer Vision (pp. 525–542). Springer.
Metadaten
Titel
Combinational Logic-Based Implementation of PUF
verfasst von
Pranesh Santikellur
Rajat Subhra Chakraborty
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-4017-0_6

Neuer Inhalt