Observations on the BayesianKeySearch with Applications to Simon and Simeck

In CRYPTO 2019, Gohr pioneered the integration of machine learning with differential cryptanalysis, demonstrating that differential-neural distinguishers can outperform classical techniques in distinguishing attacks. He also introduced a novel key-recovery strategy based on Bayesian optimization, termed BayesianKey- Search, enhancing key recovery for Speck32/64. However, the impact of parameter selection on the complexity and success probability of key-recovery attack using BayesianKeySearch remains underexplored.This paper investigates the impact of parameter selections on key-recovery effectiveness. Gohr’s key-recovery attack involves two stages, each using a cutoff value to filter candidate guesses for the last subkey and second-to-last subkey. Previous works selected these cutoffs independently. We propose connecting these cutoff selections, enhancing coordination between stages and improving the attack’s complexity and success probability.Applying our parameter optimization, we enhance the single-key recovery attacks on 16-round Simon32/64, 16-round and 17-round Simeck32/64, achieving higher success rates and lower time complexities compared to previous works. Additionally, for related-key differential-neural attacks on Simon32/64, we exploit both single-key and related-key features from cross-paired ciphertexts, developing advanced neuraldistinguishers for up to 13 rounds. Using these neural-distinguishers combined with carefully selected classical differentials, we devise an 18-round related-key recovery attack on Simon32/64. Our results validate the practical effectiveness of the proposed strategies and are expected to contribute to the advancement of machine learningaided cryptanalysis.