Abstract
At CRYPTO’19, A. Gohr proposed neural distinguishers for the lightweight block cipher Speck32/64, achieving better results than the state-of-the-art at that point. However, the motivation for using that particular architecture was not very clear; therefore, in this paper, we study the depth-10 and depth-1 neural distinguishers proposed by Gohr [7] with the aim of finding out whether smaller or better-performing distinguishers for Speck32/64 exist.
We first evaluate whether we can find smaller neural networks that match the accuracy of the proposed distinguishers. We answer this question in the affirmative with the depth-1 distinguisher successfully pruned, resulting in a network that remained within one percentage point of the unpruned network’s performance. Having found a smaller network that achieves the same performance, we examine whether its performance can be improved as well. We also study whether processing the input before giving it to the pruned depth-1 network would improve its performance. To this end, convolutional autoencoders were found that managed to reconstruct the ciphertext pairs successfully, and their trained encoders were used as a preprocessor before training the pruned depth-1 network. We found that, even though the autoencoders achieved a nearly perfect reconstruction, the pruned network did not have the necessary complexity anymore to extract useful information from the preprocessed input, motivating us to look at the feature importance to get more insights. To achieve this, we used LIME, with results showing that a stronger explainer is needed to assess it correctly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abed, F., List, E., Lucks, S., Wenzel, J.: Differential cryptanalysis of round-reduced Simon and Speck. In: Cid, C., Rechberger, C. (eds.) FSE 2014. LNCS, vol. 8540, pp. 525–545. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46706-0_27
Bank, D., Koenigstein, N., Giryes, R.: Autoencoders. CoRR abs/2003.05991 (2020). https://arxiv.org/abs/2003.05991
Beaulieu, R., Treatman-Clark, S., Shors, D., Weeks, B., Smith, J., Wingers, L.: The Simon and speck lightweight block ciphers. In: 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1–6 (2015). https://doi.org/10.1145/2744769.2747946
Benamira, A., Gerault, D., Peyrin, T., Tan, Q.Q.: A deeper look at machine learning-based cryptanalysis. In: Canteaut, A., Standaert, F.-X. (eds.) EUROCRYPT 2021. LNCS, vol. 12696, pp. 805–835. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77870-5_28
Biham, E., Shamir, A.: Differential cryptanalysis of des-like cryptosystems, vol. 4, pp. 2–21 (1990). https://doi.org/10.1007/3-540-38424-3_1
Frankle, J., Carbin, M.: The lottery ticket hypothesis: training pruned neural networks. CoRR abs/1803.03635 (2018). http://arxiv.org/abs/1803.03635
Gohr, A.: Improving attacks on round-reduced speck32/64 using deep learning. In: Boldyreva, A., Micciancio, D. (eds.) CRYPTO 2019. LNCS, vol. 11693, pp. 150–179. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26951-7_6
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Hou, Z., Ren, J., Chen, S.: Cryptanalysis of round-reduced simon32 based on deep learning. IACR Cryptol. ePrint Arch. 2021, 362 (2021)
Hou, Z., Ren, J., Chen, S.: Improve neural distinguisher for cryptanalysis. Cryptology ePrint Archive, Report 2021/1017 (2021). https://ia.cr/2021/1017
Hu, H., Peng, R., Tai, Y., Tang, C.: Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. CoRR abs/1607.03250 (2016). http://arxiv.org/abs/1607.03250
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 3149–3157. NIPS 2017, Curran Associates Inc., Red Hook, NY, USA (2017)
Lai, X., Massey, J.L., Murphy, S.: Markov ciphers and differential cryptanalysis. In: Davies, D.W. (ed.) EUROCRYPT 1991. LNCS, vol. 547, pp. 17–38. Springer, Heidelberg (1991). https://doi.org/10.1007/3-540-46416-6_2
Remy, P.: Keract: a library for visualizing activations and gradients (2020). https://github.com/philipperemy/keract
Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. KDD 2016, Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939778, https://doi.org/10.1145/2939672.2939778
Yadav, T., Kumar, M.: Differential-ml distinguisher: machine learning based generic extension for differential cryptanalysis. Cryptology ePrint Archive, Report 2020/913 (2020). https://ia.cr/2020/913
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Băcuieți, N., Batina, L., Picek, S. (2022). Deep Neural Networks Aiding Cryptanalysis: A Case Study of the Speck Distinguisher. In: Ateniese, G., Venturi, D. (eds) Applied Cryptography and Network Security. ACNS 2022. Lecture Notes in Computer Science, vol 13269. Springer, Cham. https://doi.org/10.1007/978-3-031-09234-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-09234-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09233-6
Online ISBN: 978-3-031-09234-3
eBook Packages: Computer ScienceComputer Science (R0)