More Insight on Deep Learning-aided Cryptanalysis

ePrint Report: More Insight on Deep Learning-aided Cryptanalysis

Zhenzhen Bao, Jinyu Lu, Yiran Yao, Liu Zhang

In CRYPTO 2019, Gohr showed that well-trained neural networks could perform cryptanalytic distinguishing tasks superior to differential distribution table (DDT)-based distinguishers. This suggests that the differential-neural distinguisher (ND) may use additional information besides pure ciphertext differences. However, the explicit knowledge beyond differential distribution is still unclear. In this work, we provide explicit rules that can be used alongside DDTs to enhance the effectiveness of …

ciphertext cryptanalysis crypto deep learning distribution eprint report information insight may networks neural networks report