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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.

arXiv cs.CR · 1d agoResearch

Measuring the Security of the Evolving Software Supply Chain: a Research Agenda

Researchers propose a unified cross-ecosystem measurement agenda for software supply chain security, targeting dependency modeling and AI-generated dependency patterns.

The paper argues that existing quantitative measurement and vulnerability management approaches for software supply chain security are fragmented and ecosystem-specific, limiting comparable risk assessments. It lays out a research agenda starting with a Systematization of Knowledge to expose gaps in dependency modeling, transitive dependency treatment, and real-world exploitability of vulnerabilities. It further warns that AI-assisted development with coding LLMs will create dependency patterns not captured by traditional Software Composition Analysis tools, motivating a rethink of dependency modeling.

arXiv cs.CR · 9d agoResearch