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.
- Zero-target-trace generative framework improves DL-SCA cross-device portability
- Combines structured cVAE generator with Walsh-Hadamard leakage anchors
- Physical MDM remains superior on identical electrical clones (D4)
- Evaluated on 32-bit AES_PTv2 side-channel traces
From the original article
1 min read at arxiv.orgDeep 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.
By Niloufar Sayadi · arxiv.org
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