Skip to main content
Top
Published in:
Cover of the book

2021 | OriginalPaper | Chapter

Polytopic Attack on Round-Reduced Simon32/64 Using Deep Learning

Authors : Heng-Chuan Su, Xuan-Yong Zhu, Duan Ming

Published in: Information Security and Cryptology

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In CRYPTO 2019, Gohr uses the residual network technology of artificial intelligence to build a differential distinguisher, and attacks the reduced-round Speck32/64. We tried this method to recover the keys for ten-round Simon32/64. In this paper, we have three innovations. First, we construct polytope neural network distinguisher. On eight-round Simon32/64, polytope neural network distinguisher could increase the success rate of three neural network distinguishers with 0.76 success rate to 0.92. Second, we propose an attack on Simon32/64 based on the combination of the probability of differential path and polytope neural network distinguisher. This method can only increase the computational complexity of the chosen data as the number of rounds increases. Nine-round polytope neural network distinguisher is used to filter out data, whether it is what we want. Eight-round neural distinguisher is used to recover the final round key. The computational complexity of key recovery on the final key of eleven-round Simon32/64 is \(2^{33.4}\). Third, we propose an attack called Bayesian Key Research with Error. With this attack, the computational complexity of key recovery on the final key of eleven-round Simon32/64 is \(2^{30.9}\).
In our paper, the main idea is combining polytope differences with neural networks. By constructing polytope differential neural network distinguisher, we make a key recovery attack. In order to increase the number of rounds, we first used brute force attack and then proposed Bayesian Key Research with Error. We think this idea can be applied to many cryptographic algorithms.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

Literature
1.
go back to reference Hospodar, G., Gierlichs, B., Mulder, D.E., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptogr. Eng. 1(4), 293 (2011)CrossRef Hospodar, G., Gierlichs, B., Mulder, D.E., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptogr. Eng. 1(4), 293 (2011)CrossRef
3.
go back to reference Gohr, A.: Improving attacks on round-reduced Speck32/64 using deep learning. In: International Cryptology Conference, pp. 150–179 (2019) Gohr, A.: Improving attacks on round-reduced Speck32/64 using deep learning. In: International Cryptology Conference, pp. 150–179 (2019)
4.
go back to reference Beaulieu, R., Shors, D., Smith, J., et al.: The SIMON and SPECK Families of Lightweight Block Ciphers. IACR Cryptology ePrint Archive (2013) Beaulieu, R., Shors, D., Smith, J., et al.: The SIMON and SPECK Families of Lightweight Block Ciphers. IACR Cryptology ePrint Archive (2013)
5.
go back to reference Abed, F., List, E., Lucks, S., et al.: Differential cryptanalysis of round-reduced Simon and speck. In: Fast Software Encryption, pp. 525–545 (2014) Abed, F., List, E., Lucks, S., et al.: Differential cryptanalysis of round-reduced Simon and speck. In: Fast Software Encryption, pp. 525–545 (2014)
6.
go back to reference Qiao, K., Hu, L., Sun, S., et al.: Differential analysis on Simeck and SIMON with dynamic key-guessing techniques. In: International Conference on Information Systems Security, pp. 64–85 (2016) Qiao, K., Hu, L., Sun, S., et al.: Differential analysis on Simeck and SIMON with dynamic key-guessing techniques. In: International Conference on Information Systems Security, pp. 64–85 (2016)
7.
go back to reference Lecun, Y., Bengio, Y., Hinton, G.E., et al.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef Lecun, Y., Bengio, Y., Hinton, G.E., et al.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef
8.
go back to reference Howard, A., Zhu, M., Chen, B., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. Arxiv: Computer Vision and Pattern Recognition (2017) Howard, A., Zhu, M., Chen, B., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. Arxiv: Computer Vision and Pattern Recognition (2017)
9.
go back to reference Zhang, X., Zhou, X., Lin, M., et al.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Computer Vision and Pattern Recognition, pp. 6848–6856 (2018) Zhang, X., Zhou, X., Lin, M., et al.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
10.
go back to reference He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016) He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
11.
go back to reference Pelikan, M., Goldberg, D.E., Cantupaz, E., et al.: BOA: the Bayesian optimization algorithm. In: Genetic and Evolutionary Computation Conference, pp. 525–532 (1999) Pelikan, M., Goldberg, D.E., Cantupaz, E., et al.: BOA: the Bayesian optimization algorithm. In: Genetic and Evolutionary Computation Conference, pp. 525–532 (1999)
12.
go back to reference Lawler, B.: Computational complexity: a conceptual perspective written by Oded Goldreich, and published by Cambridge University Press, 606 p. (2008). ISBN 978-0-521-88473-0. ACM SIGSOFT Softw. Eng. Notes 35(1), 37–38 (2010) Lawler, B.: Computational complexity: a conceptual perspective written by Oded Goldreich, and published by Cambridge University Press, 606 p. (2008). ISBN 978-0-521-88473-0. ACM SIGSOFT Softw. Eng. Notes 35(1), 37–38 (2010)
13.
go back to reference Wang, N., Wang, X., Jia, K., et al.: Differential attacks on reduced SIMON versions with dynamic key-guessing techniques. Sci. China Ser. F: Inf. Sci. 61(9), 1–3 (2018)MathSciNet Wang, N., Wang, X., Jia, K., et al.: Differential attacks on reduced SIMON versions with dynamic key-guessing techniques. Sci. China Ser. F: Inf. Sci. 61(9), 1–3 (2018)MathSciNet
14.
go back to reference Tiessen, T.: Polytopic cryptanalysis. In: International Cryptology Conference, pp. 214–239 (2016) Tiessen, T.: Polytopic cryptanalysis. In: International Cryptology Conference, pp. 214–239 (2016)
17.
go back to reference Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics. IEEE (2013) Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics. IEEE (2013)
18.
go back to reference Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Computer Science (2014) Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Computer Science (2014)
Metadata
Title
Polytopic Attack on Round-Reduced Simon32/64 Using Deep Learning
Authors
Heng-Chuan Su
Xuan-Yong Zhu
Duan Ming
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-71852-7_1

Premium Partner