Liu Jing Zhang

Feb 25, 2024 | eprint.iacr.org | Liu Jing Zhang |Zilong Wang |Weixi Zheng

Paper 2024/322 , Xidian University, Xidian University, Xidian UniversityAbstract At CRYPTO 2019, Gohr demonstrated that differential-neural distinguishers (DNDs) for Speck32/64 can learn more features than classical cryptanalysis's differential distribution tables (DDT). Furthermore, a non-classical key recovery procedure is devised by combining the Upper Confidence Bound (UCB) strategy and the BayesianKeySearch algorithm.

May 2, 2023 | eprint.iacr.org | Liu Jing Zhang |Zilong Wang |Jian Guo |Baocang Wang

Paper 2022/183 Improving Differential-Neural Cryptanalysis with Inception , Xidian University, Xidian University, Nanyang Technological University, Xidian UniversityAbstract In CRYPTO'19, Gohr proposed a new cryptanalysis method by building differential-neural distinguishers with neural networks. Gohr combined a differential-neural distinguisher with a classical differential path and achieved a 12-round (out of 22) key recovery attack on Speck32/64.