s-MDM: Generative Virtualization of Multi-Device Hardware Variations for Portable DL-SCA
Researchers present s-MDM, a generative framework synthesizing virtual device profiles to improve cross-device portability of deep learning side-channel analysis.
The poster introduces the Synthetic Multiple Device Model (s-MDM), a zero-target-trace generative framework addressing performance degradation of deep learning side-channel analysis on unseen hardware. It combines a structured cVAE generator, Walsh-Hadamard leakage anchors, continuous style modulation, and decoupled leakage-style-domain critics to synthesize virtual source-device profiles offline. Benchmarked on 32-bit AES_PTv2 traces, s-MDM achieves consistently low key rank on layout- and acquisition-shifted Pinata targets where physical baselines are unstable.
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