A Deeper Look at Machine Learning-Based Cryptanalysis
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Benamira noted that Gohr's differential-neural distinguisher provides a robust approximation of the cipher's differential distribution table (DDT) and learns additional information beyond the DDT.
“At EUROCRYPT 2021, Benamira [2] noted that Gohr's differential-neural distinguisher provides a robust approximation of the cipher's differential distribution table (DDT) and learns additional information beyond the DDT.”
From Differential-Neural Cryptanalysis on AES (2024) · cites this paper as [2]
Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works
Polynomial calculation of the Shapley value based on sampling
Castro, J., Gómez, D., & Tejada, J. (2009) · Computers & Operations Research · doi:10.1016/j.cor.2008.04.004 · Cited by 601 · see citing works
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Differential-Neural Cryptanalysis on AES
ZHANG, L., WANG, Z., & LU, J. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2024edl8044 · Cited by 4 · see citing works
- Authors
- Benamira, A., Gerault, D., Peyrin, T., & Tan, Q. Q.
- Affiliations
- Nanyang Technological University (SG)
- Corresponding author
- Benamira, Adrien
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Volume
- 2021
- Pages
- 805-835
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3159555774
- Cited by
- 130
- References
- 3
- Citations by year
- 2026: 21 · 2025: 24 · 2024: 37 · 2023: 25 · 2022: 19 · 2021: 4
- FWCI
- 9.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- IACR Cryptology ePrint Archive (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptographic Implementations and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Coding theory and cryptography
- Keywords
- SPECK block cipher, differential cryptanalysis, deep neural network interpretability, key recovery attack, differential distributions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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