Problem
Research questions and friction points this paper is trying to address.
Deep Learning-based Side-Channel Analysis
cross-device portability
hardware variations
performance degradation
s-MDM: Generative Virtualization of Multi-Device Hardware Variations for Portable DL-SCA
🤖 AI Summary
为解决跨设备性能下降问题，提出s-MDM框架，通过生成虚拟硬件配置文件提高DL-SCA的可移植性。
📝 Abstract
Deep Learning-based Side-Channel Analysis (DL-SCA) frequently suffers from catastrophic performance degradation across unseen hardware due to printed circuit board routing differences, silicon process variations, and measurement noise shifts. This poster presents the Synthetic Multiple Device Model (s-MDM), a zero-target-trace generative framework designed to improve cross-device portability. s-MDM combines a structured cVAE generator, a Walsh-Hadamard leakage anchor, continuous style modulation, and decoupled leakage-style--domain critics to synthesize virtual source-device profiles offline. Benchmarked on 32-bit side-channel traces (AES_PTv2), s-MDM maps a precise operational boundary: while physical MDM remains superior on identical electrical clones (D4), s-MDM achieves consistently low key rank on the layout/acquisition-shifted Pinata target, where physical baselines are unstable or misaligned.
Innovation
Methods, ideas, or system contributions that make the work stand out.
zero-target-trace generative framework
structured cVAE generator
Walsh-Hadamard leakage anchor
continuous style modulation
decoupled leakage-style-domain critics
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