Improved Differential-neural Cryptanalysis for Round-reduced Simeck32/64. (arXiv:2301.11601v1 [cs.CR])

In CRYPTO 2019, Gohr presented differential-neural cryptanalysis by building
the differential distinguisher with a neural network, achieving practical 11-,
and 12-round key recovery attack for Speck32/64. Inspired by this framework, we
develop the Inception neural network that is compatible with the round function
of Simeck to improve the accuracy of the neural distinguishers, thus improving
the accuracy of (9-12)-round neural distinguishers for Simeck32/64. To provide
solid baselines for neural distinguishers, we compute the full distribution of
differences induced by one specific input difference up to 13-round
Simeck32/64. Moreover, the performance of the DDT-based distinguishers in
multiple ciphertext pairs is evaluated. Compared with the DDT-based
distinguishers, the 9-, and 10-round neural distinguishers achieve better
accuracy. Also, an in-depth analysis of the wrong key response profile revealed
that the 12-th and 13-th bits of the subkey have little effect on the score of
the neural distinguisher, thereby accelerating key recovery attacks. Finally,
an enhanced 15-round and the first practical 16-, and 17-round attacks are
implemented for Simeck32/64, and the success rate of both the 15-, and 16-round
attacks is almost 100%.