  In CRYPTO'19, Gohr proposed a new cryptanalysis strategy using machine
learning algorithms. Combining the differential-neural distinguisher with a
differential path and integrating the advanced key recovery procedure, Gohr
achieved a 12-round key recovery attack on Speck32/64. Chen and Yu improved
prediction accuracy of differential-neural distinguisher considering derived
features from multiple-ciphertext pairs instead of single-ciphertext pairs. By
modifying the kernel size of initial convolutional layer to capture more
dimensional information, the prediction accuracy of differential-neural
distinguisher can be improved for for three reduced symmetric ciphers. For DES,
we improve the prediction accuracy of (5-6)-round differential-neural
distinguisher and train a new 7-round differential-neural distinguisher. For
Chaskey, we improve the prediction accuracy of (3-4)-round differential-neural
distinguisher. For PRESENT, we improve the prediction accuracy of (6-7)-round
differential-neural distinguisher. The source codes are available in
https://drive.google.com/drive/folders/1i0RciZlGZsEpCyW-wQAy7zzJeOLJNWqL?usp=sharing.
