Paper 2023/1391
More Insight on Deep Learning-aided Cryptanalysis
Abstract
In CRYPTO 2019, Gohr showed that well-trained neural networks could perform cryptanalytic distinguishing tasks superior to differential distribution table (DDT)-based distinguishers. This suggests that the differential-neural distinguisher (ND) may use additional information besides pure ciphertext differences. However, the explicit knowledge beyond differential distribution is still unclear. In this work, we provide explicit rules that can be used alongside DDTs to enhance the effectiveness of distinguishers compared to pure DDT-based distinguishers. These rules are based on strong correlations between bit values in right pairs of XOR-differential propagation through addition modulo
Note: Full version of AC2023
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- A major revision of an IACR publication in ASIACRYPT 2023
- Keywords
- Neural NetworkInterpretabilityModular AdditionRelated-keySpeck
- Contact author(s)
-
zzbao @ tsinghua edu cn
jinyu_smile @ foxmail com
yiran005 @ e ntu edu sg
liuzhang @ stu xidian edu cn - History
- 2023-09-18: approved
- 2023-09-18: received
- See all versions
- Short URL
- https://ia.cr/2023/1391
- License
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CC BY
BibTeX
@misc{cryptoeprint:2023/1391, author = {Zhenzhen Bao and Jinyu Lu and Yiran Yao and Liu Zhang}, title = {More Insight on Deep Learning-aided Cryptanalysis}, howpublished = {Cryptology {ePrint} Archive, Paper 2023/1391}, year = {2023}, url = {https://eprint.iacr.org/2023/1391} }