Enhancing Model Generalization for Efficient Cross-Device Side-Channel Analysis

Deep learning (DL)-based techniques have garnered significant attention as an innovative method for profiled side-channel analysis (SCA). Despite their proven effectiveness, recent studies have highlighted challenges faced by DL-based profiled attacks in a more realistic portability threat model, where two devices are used respectively for profiling and the attack. In this paper, we propose a novel approach for cross-device attack by incorporating the Denoising Diffusion Probabilistic Model (DDPM) to develop a generalized model. Additionally, an adaptive multi-task loss is employed to balance multiple training objectives that respectively focus on model generalization and precision. We evaluate our strategy on five cross-device SCA datasets. The experimental results show that, compared to baseline methods, our approach achieves significantly enhanced performance, as measured by the number of traces required to recover the secret key. Specifically, on a more challenging dataset obtained from three SAKURA-G evaluation boards, our method successfully recovers the secret key using approximately 300 traces, whereas baseline methods fail to guarantee a successful cross-device attack even with 5,000 traces. Furthermore, our method demonstrates remarkably enhanced attack efficiency, reducing attack time by over an hour compared to the baselines.