Angshuman Karmakar

h-index15
2papers

2 Papers

CRMar 13, 2024
SNOW-SCA: ML-assisted Side-Channel Attack on SNOW-V

Harshit Saurabh, Anupam Golder, Samarth Shivakumar Titti et al.

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.

CRJan 19, 2022
A 333.9uW 0.158mm$^2$ Saber Learning with Rounding based Post-Quantum Crypto Accelerator

Archisman Ghosh, J. M. B. Mera, Angshuman Karmakar et al.

National Institute of Standard & Technology (NIST) is currently running a multi-year-long standardization procedure to select quantum-safe or post-quantum cryptographic schemes to be used in the future. Saber is the only LWR based algorithm to be in the final of Round 3. This work presents a Saber ASIC which provides 1.37X power-efficient, 1.75x lower area, and 4x less memory implementation w.r.t. other SoA PQC ASIC. The energy-hungry multiplier block is 1.5x energyefficient than SoA.