Paper 2021/719
Conditional Differential-Neural Cryptanalysis
Zhenzhen Bao and Jian Guo and Meicheng Liu and Li Ma and Yi Tu
Abstract
In CRYPTO 2019, Gohr introduced deep learning into cryptanalysis, and for the first time successfully applied it to key recovery attacks on Speck32/64 reduced to 11 and 12 rounds, with complexities comparable with traditional differential cryptanalysis. In this paper, we introduce the technique of generalized neutral bits into Gohr's framework, and successfully mount the first practical key recovery attacks against 13-round Speck32/64 with time $2^{48}$ and data $2^{29}$ for a success rate of 0.21. Compared against the best differential attacks in literature with time $2^{51}$ for 12 rounds or impractical time $2^{57}$ on a single GPU for 13 rounds, the full implementation of our 13-round attack is able to complete execution within 3 days. We also extend the framework to Simon32/64, and reduce the data complexity for the practical 16-round attack from 1/6 of the codebook to $2^{21}$. This is arguably the first time to witness deep learning based cryptanalysis having a considerable advantage over traditional methods.
Metadata
- Available format(s)
- Category
- Secret-key cryptography
- Publication info
- Preprint. MINOR revision.
- Keywords
- Neural DistinguisherKey Recovery AttackDifferential CryptanalysisSimonSpeckGeneralized Neutral BitsBayesian Search
- Contact author(s)
- zzbao @ ntu edu sg,guojian @ ntu edu sg,meicheng liu @ gmail com,skloismary @ gmail com,tuyi0002 @ e ntu edu sg
- History
- 2022-09-21: last of 3 revisions
- 2021-05-31: received
- See all versions
- Short URL
- https://ia.cr/2021/719
- License
-
CC BY