Conclusion
In this study, we have developed a neural network aimed at enhancing the precision of neural distinguishers, demonstrating its capability to surpass DDT-based distinguishers in certain rounds. To extend the scope of our key recovery attack to additional rounds, we have diligently focused on improving both classical differentials and neural distinguishers. Consequently, we have successfully executed practical key recovery attacks on SIMECK32/64, effectively advancing the practical attack threshold by two additional rounds, allowing us to reach up to 17 rounds.
This is a preview of subscription content, log in via an institution to check access.
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Gohr A. Improving attacks on round-reduced speck32/64 using deep learning. In: Proceedings of the 39th Annual International Cryptology Conference. 2019, 150–179
Lyu L, Tu Y, Zhang Y. Deep learning assisted key recovery attack for round-reduced simeck32/64. In: Proceedings of the 25th International Conference on Information Security. 20 22, 443–463
Zhang L, Wang Z, Wang B. Improving differential-neural cryptanalysis with inception blocks. IACR Cryptology ePrint Archive, 2022, 183
Bao Z, Guo J, Liu M, Ma L, Tu Y. Enhancing differential-neural cryptanalysis. In: Proceedings of the 28th International Conference on the Theory and Application of Cryptology and Information Security. 2022, 318–347
Bellini E, Gérault D, Hambitzer A, Rossi M. A cipher-agnostic neural training pipeline with automated finding of good input differences. IACR Transaction on Symmetric Cryptology. 2023, 184–212
Benamira A, Gerault D, Peyrin T, Tan Q Q. A deeper look at machine learning-based cryptanalysis. In: Proceedings of the 40th Annual International Conference on the Theory and Applications of Cryptographic Techniques. 2021, 805–835
Gohr A, Leander G, Neumann P. An assessment of differential-neural distinguishes. In AICrypt’23 — 3rd Workshop on Artificial Intelligence and Cryptography. 2023
Hou Z Z, Ren J J, Chen S Z. Improve neural distinguishes of SIMON and SPECK. Security and Communication Networks, 2021, 2021: 9288229
Lu J, Liu G, Liu Y, Sun B, Li C, Liu L. Improved neural distinguishers with (related-key) differentials: applications in SIMON and SIMECK. 2022, arXiv preprint arXiv: 2201.03767
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62172319, 62172427), the Fundamental Research Funds for the Central Universities (No. QTZX23090) and the Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX20220016).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Zhang, L., Lu, J., Wang, Z. et al. Improved differential-neural cryptanalysis for round-reduced SIMECK32/64. Front. Comput. Sci. 17, 176817 (2023). https://doi.org/10.1007/s11704-023-3261-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11704-023-3261-z