基于机器学习的分组密码结构识别

doi:10.11959/j.issn.2096-109x.2023040

Abstract

Abstract:

Cryptographic identification is a critical aspect of cryptanalysis and a fundamental premise for key recovery.With the advancement of artificial intelligence, cryptanalysis based on machine learning has become increasingly mature, providing more effective methods and valuable insights for cryptographic identification.The distinguishability experiments were performed based on the Machine Learning to identify the structures of block ciphers in conditions of random keys.The identification of two structures of block ciphers from theoretical and experimental angles was studied.The differences of features in two structures’ cipher texts have been deduced by introducing the runs distribution index, feature distribution functions, KL-divergence, etc.After completing the feasibility research, experiments to identify the structures of two block ciphers using two Machine Learning models and the runs distribution index were conducted.The experiments were divided into two groups: single algorithm group and mixture algorithms group.It is found that the accuracy of both groups are more than 80%, which is around 40% higher than former work.The problem of identifying the structures of Block Ciphers in the conditions of random keys is solved in detail.Meanwhile, differences between the two structures of block ciphers are verified, which can serve as a reference for the design of cryptography algorithms.

Key words: block ciphers, machine learning, feature indices, probabilities and statistic, cryptography identification

CLC Number:

TP393

Ruiqi XIA, Manman LI, Shaozhen CHEN. Identification on the structures of block ciphers using machine learning[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 79-89.

Figures/Tables 14

References 30

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SPN结构	Feistel结构
AES-128^[20]	DES^[30]
Serpent-128^[21]	SIMON-64^[27]
SKINNY-64^[22]	Speck-32^[27]
PRESENT^[23]	KASUMI^[28]
Midori-64^[24]	Twofish^[26]
ARIA-128^[25]	TWINE^[29]

密文特征算法	标签
AES-128	S1
Serpent-128	S2
SKINNY-64	S3
PRESENT	S4
Midori-64	S5
ARIA-128	S6
DES	F1
SIMON-64	F2
Speck-32	F3
KASUMI	F4
Twofish	F5
TWINE	F6

—	F1	F2	F3	F4	F5	F6
S1	88.2%	96.7%	95.6%	91.2%	90.7%	92.3%
S2	93.4%	97.8%	98.9%	98.9%	99.0%	92.7%
S3	99.3%	97.8%	97.0%	94.5%	91.3%	90.8%
S4	91%	81.4%	86.2%	93.7%	95.7%	97.6%
S5	91.2%	97.3%	97.3%	88.5%	96.4%	95.9%
S6	91.1%	91.0%	89.2%	89.3%	92.7%	93.8%

—	F1	F2	F3	F4	F5	F6
S1	92.2%	96.7%	95.6%	99%	99.7%	92.3%
S2	97.8%	97.8%	98.9%	98.9%	99.0%	92.7%
S3	99.3%	97.8%	97.0%	94.5%	91.3%	99.8%
S4	91%	95.2%	99.5%	93.7%	95.7%	97.6%
S5	91.2%	97.3%	97.3%	96.2%	96.4%	95.9%
S6	99.1%	88.9%	97.0%	99.3%	99.7%	99.8%

模型	准确率
随机森林模型	90.1%
Adaboosting模型	92.9%

Identification on the structures of block ciphers using machine learning

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