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Jan, 2023
针对轮归约Simeck32/64的改进差分神经密码分析
Improved Differential-neural Cryptanalysis for Round-reduced Simeck32/64
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Liu Zhang, Jinyu Lu, Zilong Wang, Chao Li
TL;DR
本研究通过构建具有神经网络特点的差分区分器,成功改进了Simeck32/64的(9-12)轮的神经区分器的准确度,并在Simeck32/64上实现了15轮、16轮和17轮实用的密钥恢复攻击,成功率接近100%。
Abstract
In CRYPTO 2019, Gohr presented
differential-neural cryptanalysis
by building the differential distinguisher with a
neural network
, achieving practical 11-, and 12-round
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