Paper 2024/428

SNOW-SCA: ML-assisted Side-Channel Attack on SNOW-V

Harshit Saurabh, Indian Institute of Science Bangalore
Anupam Golder, Georgia Institute of Technology
Samarth Shivakumar Titti, Indian Institute of Science Bangalore
Suparna Kundu, KU Leuven
Chaoyun Li, University of Surrey
Angshuman Karmakar, Indian Institute of Technology Kanpur
Debayan Das, Indian Institute of Science Bangalore
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)
PDF
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
Creative Commons Attribution
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}
}
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