几类密码算法的神经网络差分区分器的改进

doi:10.19665/j.issn1001-2400.20230212

Abstract

Abstract:

In order to further study the application of the neural network in cryptanalysis,the neural network differential divider of several typical lightweight block cipher algorithms is constructed and improved by using a deep residual network and traditional differential cryptanalysis techniques.The main results are as follows.First,the neural distinguishers of 4 to 7 rounds of PRESENT,3 rounds of KLEIN,7 to 9 rounds of LBlock and 7 to 10 rounds of Simeck32/64 are constructed and analyzed respectively based on the block cipher structure.Second,based on the characteristics of SPN structure block ciphers,PRESENT and KLEIN's neural distinguishers are improved,which can improve the accuracy of about 5.12% at most.In the study of LBlock’s neural distinguisher,it is verified that this improved method is not suitable for Feistel structure block ciphers.Third,based on the characteristics of the simeck 32/64 cryptography algorithm,the neural distinguisher is improved,with the accuracy improved by 2.3%.Meanwhile,the improved method of Simeck 32/64 is combined with the polyhedral difference analysis,and the accuracy of the existing 8-round and 9-round Simeck 32/64 poly neural network difference partition is increased by 1% and 3.2%.Finally,the three types of neural distinguishers obtained in the experiment are applied to the last round key recovery attack of 11-round simeck 32/64,with the best experimental result being a 99.4% success rate with 26.6 data complexity in 1 000 attacks.

Key words: neural differential distinguisher, lightweight block ciphers, partial key recovery attacks

CLC Number:

TP309.7

YANG Xiaoxue, CHEN Jie. Improvement of the neural distinguishers of several ciphers[J].Journal of Xidian University, 2024, 51(1): 210-222.

Figures/Tables 17

符号	含义
→	趋近于
<<<	循环左移
☉	按位与运算
⊕	按位异或运算
Num_block	分组数量
Block_size	分组大小
Word_size	Block_size /2
$F 2 n$	n比特向量集合

References 30

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轮数	PRESENT测试准确率	KLEIN测试准确率
3		0.886 0
4	0.996 0
5	0.849 8
6	0.658 7
7	0.548 0

密码	轮数	N₈准确率 (验证模型)	N₁₆准确率 (改进模型)	N₃₂准确率 (原始模型)	N₆₄准确率 (验证模型)	文献[22]准确率 (2倍样本数)
KLEIN	3	0.883 6	0.937 2	0.886 0	0.891 2
PRESENT	4	0.996 5	0.998 3	0.997 0	0.996 3
PRESENT	5	0.848 5	0.855 1	0.849 8	0.848 1
PRESENT	6	0.658 3	0.662 3	0.658 7	0.657 5	0.719 8
PRESENT	7	0.545 0	0.550 5	0.548 6	0.548 5	0.550 3

轮数	N₈准确率	N₁₆准确率	N₃₂准确率
7	0.980 1	0.988 1	0.999 3
8	0.721 1	0.800 0	0.901 4
9	0.500 0	0.501 0	0.632 9

模型	轮数	准确率	来源
T_N	7	0.997 3	本节对文献[24]的复现
T_Ns	7	0.999 8	本节结果
T_N	8	0.967 7	文献[24]
T_Ns	8	0.977 4	本节结果
T_N	9	0.758 5	文献[24]
T_Ns	9	0.780 3	本节结果
T_N	10	0.586 1	本节对文献[24]的复现
T_Ns	10	0.602 2	本节结果

模型	明密文对数	攻击成功率/%
Ns	2^6.6	82.6
T_N	2^6.6	53.0
T_Ns	2^6.6	99.4

Improvement of the neural distinguishers of several ciphers

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Figures/Tables 17

References 30

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轮数	N准确率(原始模型)	N_s准确率(4.2.2节)	T_N准确率(文献[24])	T_Ns准确率(4.2.3节)
7	0.988 2	0.991 6	0.997 3	0.999 8
8	0.877 7	0.901 4	0.967 7	0.977 4
9	0.675 4	0.706 8	0.758 5	0.780 3
10	0.541 5	0.569 0	0.586 1	0.602 2

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