SNOW-SCA: ML-assisted Side-Channel Attack on SNOW-V
Harshit Saurabh Anupam Golder Samarth Shivakumar Titti Suparna Kundu Chaoyun Li Angshuman Karmakar Debayan Das
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
\textit{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.