Improved Differential-neural Cryptanalysis for Round-reduced Simeck32/64

TL;DR

This paper advances neural cryptanalysis techniques for Simeck32/64, achieving near-perfect key recovery for 15-17 rounds by developing an improved neural network and analyzing key response profiles.

Contribution

It introduces an Inception neural network tailored for Simeck, enhancing the accuracy of neural distinguishers and enabling practical attacks on more rounds than previous methods.

Findings

Neural distinguishers outperform DDT-based methods for 9-10 rounds.

High success rate (~100%) for 15-16 round key recovery attacks.

Full distribution analysis of differences up to 13 rounds for Simeck32/64.

Abstract

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%.