Improved differential-neural cryptanalysis for round-reduced SIMECK32/64  

作　　者：Liu ZHANG Jinyu LU Zilong WANG Chao LI 

机构地区：[1]School of Cyber Engineering,Xidian University,Xi’an 710126,China [2]State Key Laboratory of Cryptology,Beijing 100878,China [3]College of Sciences,National University of Defense Technology,Changsha 410073,China

出　　处：《Frontiers of Computer Science》2023年第6期187-189,共3页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.62172319,62172427);the Fundamental Research Funds for the Central Universities(No.QTZX23090);the Postgraduate Scientific Research Innovation Project of Hunan Province(No.CX20220016).

摘　　要：1 Introduction In CRYPTO 2019,Gohr[1]innovatively integrated deep learning with differential cryptanalysis,specifically applied to SPECK32/64,resulting in the development of a neural distinguisher(ND)that outperforms the DDT-based distinguisher(DD).Subsequently,a hybrid distinguisher(HD)was introduced,combining the strengths of ND and a classical differential(CD)and the practical realization of 11-and 12-round key recovery attacks is launched.In 2022,Lyu et al.[2]further enhanced Gohr's framework,applying it to SIMECK32/64.To more deeply evaluate the security of SIMECK32/64,we made some improvements for differentialneural cryptanalysis,as listed below.

关 键 词：NEURAL DIFFERENTIAL analysis 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]