Less is More: Latent Diffusion for Efficient IoT Side-Channel Analysis

The proliferation of cryptographic primitives in resource-constrained Internet of Things (IoT) devices has made them prime targets for Side-Channel Analysis (SCA). However, designing effective defenses against these attacks has become increasingly complex, as traditional deep learning approaches rely heavily on extensive profiling datasets that are difficult to obtain in the context of widely distributed and physically restricted IoT environments. This challenge is further exacerbated by countermeasures such as clock jitter and random delays. To overcome this limitation, this paper introduces a novel and data-efficient three-stage framework for generating high-fidelity synthetic side-channel traces. First, we employ a Supervised Variational Autoencoder (S-VAE) to map noisy, high-dimensional raw traces into a compact and denoised latent space, effectively creating an information-rich manifold. Second, a conditional Denoising Diffusion Implicit Model (DDIM), powered by an advanced attention-augmented U-Net, is trained exclusively on this computationally tractable latent space to learn the complex conditional data distribution. Finally, we empirically validate our framework on the public ASCAD benchmark and ChipWhisperer CW308T UFO platform. The results are compelling: an attack model trained solely on our synthetic data successfully recovers the secret key in the most challenging ASCAD_desync100 scenario using only 4107 traces and using only 2560 traces, 97.7% accuracy can be achieved on the Chipwhisphere platform. This work provides a practical and efficient pathway for the robust security evaluation of cryptographic implementations in data-scarce IoT environments, significantly lowering the barrier for thorough side-channel vulnerability analysis. © 2026 IEEE.