Paper 2024/428
SNOW-SCA: ML-assisted Side-Channel Attack on SNOW-V
Abstract
This paper presents SNOW-SCA, the first power side-channel analysis (SCA) attack of a 5G mobile communication security standard candidate, SNOW-V, running on a 32-bit ARM Cortex-M4 microcontroller. First, we perform a generic known-key correlation (KKC) analysis to identify the leakage points. Next, a correlation power analysis (CPA) attack is performed, which reduces the attack complexity to two key guesses for each key byte. The correct secret key is then uniquely identified utilizing linear discriminant analysis (LDA). The profiled SCA attack with LDA achieves 100% accuracy after training with < 200 traces, which means the attack succeeds with just a single trace. Overall, using the combined CPA and LDA attack model, the correct secret key byte is recovered with < 50 traces collected using the ChipWhisperer platform. The entire 256-bit secret key of SNOW-V can be recovered incrementally using the proposed SCA attack. Finally, we suggest low-overhead countermeasures that can be used to prevent these SCA attacks.
Metadata
- Available format(s)
- Category
- Attacks and cryptanalysis
- Publication info
- Published elsewhere. Minor revision. HOST 2024
- DOI
- 10.1109/HOST55342.2024.10545384
- Keywords
- SNOW-VSide-Channel AnalysisCorrelation Power AttackLinear Discriminant AnalysisCountermeasures
- Contact author(s)
-
harshitsaura @ iisc ac in
anupam golder @ intel com
samarthst7 @ gmail com
suparnakundu1995 @ gmail com
c li @ surrey ac uk
angshuman @ cse iitk ac in
debayandas @ iisc ac in - History
- 2024-06-18: revised
- 2024-03-12: received
- See all versions
- Short URL
- https://ia.cr/2024/428
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2024/428, author = {Harshit Saurabh and Anupam Golder and Samarth Shivakumar Titti and Suparna Kundu and Chaoyun Li and Angshuman Karmakar and Debayan Das}, title = {{SNOW}-{SCA}: {ML}-assisted Side-Channel Attack on {SNOW}-V}, howpublished = {Cryptology {ePrint} Archive, Paper 2024/428}, year = {2024}, doi = {10.1109/HOST55342.2024.10545384}, url = {https://eprint.iacr.org/2024/428} }