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

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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

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