Open Access

ARTICLE

RFA-SCA: Robust Feature Alignment for Side-Channel Analysis via Multi-Order Moment Alignment

Yuanzhen Wang¹, Hongxin Zhang^2,3,*, Shaofei Sun¹, Yaqi Zhang², Xing Fang⁴, Zhi Sun²

1 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China
2 School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
3 Beijing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and Telecommunications, Beijing, China
4 Beijing Institute of Computer Technology and Applications, Beijing, China

* Corresponding Author: Hongxin Zhang. Email:

Computers, Materials & Continua 2026, 88(3), 26 https://doi.org/10.32604/cmc.2026.081308

Received 27 February 2026; Accepted 14 May 2026; Issue published 23 July 2026

Abstract

The effectiveness of profiling deep learning side-channel attacks relies on the assumption that training and attack data follow the same distribution. However, when the profiling device differs from the target device, process-voltage-temperature (PVT) variations and clock jitter countermeasures cause distribution shifts in power traces, rendering models trained on the source device ineffective on the target. Existing domain adaptation methods typically rely on a single distributional constraint without jointly constraining kernel mean embeddings and covariance structure, thus limiting their effectiveness against strong defenses such as clock jitter. We propose Robust Feature Alignment for Side-Channel Analysis (RFA-SCA), an unsupervised domain adaptation framework that aligns source and target feature distributions without requiring the target-device key during training. RFA-SCA combines three loss components. MMD loss reduces distributional discrepancy via kernel mean embeddings in a reproducing kernel Hilbert space (RKHS), CORAL loss regularizes covariance-level feature discrepancies, and conditional entropy loss optimizes decision boundaries in the target domain. In randomized attack trials using a finite number of target-domain traces on four benchmark datasets (ASCAD, CHES CTF 2018, SAKURA-G, XMEGA), RFA-SCA achieves 100% key recovery across all six cross-device and countermeasure scenarios. Furthermore, RFA-SCA reduces the number of traces required for successful attacks by 14%–33% compared to the best baseline in the ASCAD Desync, ASCAD Gaussian Noise, and SAKURA-G scenarios. A systematic ablation study over seven loss-component variants across all six scenarios further supports the role and complementarity of the three components.

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Keywords

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