Paper 2026/606

PD-Net: Learning Device-Invariant Representations for Heterogeneous Cross-Device Side-Channel Attacks

Abstract

Heterogeneous cross-device side-channel attacks remain a critical yet underexplored challenge, as models trained on one device often fail to generalize across architectures. This paper presents PD-Net, a domain generalization framework that learns device-invariant features by disentangling algorithmic content from device-specific style and aligning feature distributions using prototypical and Maximum Mean Discrepancy (MMD) losses. PD-Net is trained on nine heterogeneous source domains spanning ARM/AVR/FPGA and power/electromagnetic leakage modalities, including 32-bit ARM Cortex-M0/M1/M3/M4, 8-bit AVR ATmega (three series), and 128-bit Xilinx Virtex-5 FPGA, and evaluated in a zero-shot setting without target-specific adaptation. Experimental results demonstrate robust zero-shot cross-architecture transfers between 8-bit and 32-bit devices, with consistent gains over existing generalization and transfer-learning approaches. In particular, PD-Net delivers 29 successful attacks with only 10 divergences across 70 settings, markedly outperforming the state of the art, which succeeds in only 4 cases and diverges 19 times. To the best of our knowledge, this is the first domain generalization (DG)-based deep learning framework to systematically demonstrate practical zero-shot heterogeneous cross-device side-channel attacks.