# Improved (Related-key) Differential-based Neural Distinguishers for SIMON and SIMECK Block Ciphers

arXiv: https://arxiv.org/abs/2201.03767 · HTML: https://mlsanity.com/paper/2201.03767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)

## Built on by (most cited first, 1 of 1)

- [Do LLMs Make Neural Distinguishers Wise?](https://mlsanity.com/paper/2606.10692/) (2026)

## Abstract (arXiv)

In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher SPECK32/64, achieving higher accuracy than the pure differential distinguishers. By its very nature, mining effective features in data plays a crucial role in data-driven deep learning. In this paper, in addition to considering the integrity of the information from the training data of the ciphertext pair, domain knowledge about the structure of differential cryptanalysis is also considered into the training process of deep learning to improve the performance. Meanwhile, taking the performance of the differential-neural distinguisher of SIMON32/64 as an entry point, we investigate the impact of input difference on the performance of the hybrid distinguishers to choose the proper input difference. Eventually, we improve the accuracy of the neural distinguishers of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128. We also obtain related-key differential-based neural distinguishers on round-reduced versions of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128 for the first time.