Improved differential-neural cryptanalysis for round-reduced SIMECK32/64

Liu ZHANG, Jinyu LU, Zilong WANG, Chao LI

PDF(841 KB)
PDF(841 KB)
Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (6) : 176817. DOI: 10.1007/s11704-023-3261-z
Information Security
LETTER

Improved differential-neural cryptanalysis for round-reduced SIMECK32/64

Author information +
History +

Graphical abstract

Cite this article

Download citation ▾
Liu ZHANG, Jinyu LU, Zilong WANG, Chao LI. Improved differential-neural cryptanalysis for round-reduced SIMECK32/64. Front. Comput. Sci., 2023, 17(6): 176817 https://doi.org/10.1007/s11704-023-3261-z

References

[1]
Gohr A. Improving attacks on round-reduced speck32/64 using deep learning. In: Proceedings of the 39th Annual International Cryptology Conference. 2019, 150–179
[2]
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. 2022, 443–463
[3]
Zhang L, Wang Z, Wang B. Improving differential-neural cryptanalysis with inception blocks. IACR Cryptology ePrint Archive, 2022, 183
[4]
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
[5]
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
[6]
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
[7]
Gohr A, Leander G, Neumann P. An assessment of differential-neural distinguishers. In AICrypt'23 – 3rd Workshop on Artificial Intelligence and Cryptography. 2023
[8]
Hou Z Z, Ren J J, Chen S Z . Improve neural distinguishers of SIMON and SPECK. Security and Communication Networks, 2021, 2021: 9288229
[9]
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).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

RIGHTS & PERMISSIONS

2023 Higher Education Press
AI Summary AI Mindmap
PDF(841 KB)

Accesses

Citations

Detail

Sections
Recommended

/