🤖 AI Summary
This study addresses the instability of side-channel leakage detection caused by variations in electromagnetic probe placement, which severely limits the transferability of cross-device attacks. To overcome this challenge, the authors propose a unified deep learning model trained on electromagnetic traces collected simultaneously from multiple probe positions, enabling, for the first time, effective capture of leakage information across a larger area of the target chip. This approach significantly enhances the robustness and transferability of side-channel attacks under varying probe locations and across different devices. The method’s stability and effectiveness in cross-environment attack scenarios are rigorously validated using datasets acquired independently in two distinct laboratories.
📝 Abstract
Side Channel Analysis (SCA) relaxes the black-box assumption of conventional cryptanalysis by incorporating physical measurements acquired during cryptographic operations. Electro-magnetic (EM) emissions of a chip during computations often provide a very valuable source of side channel leakage. During the evaluation of a chip for electro-magnetic side channel emissions one needs to position an electro-magnetic probe in an advantageous position relative to the chip. Previous literature focused on hot-spot finding and to a lower extend repositioning. Trace augmentations have been considered to aid portability of profiling using one physical device and attacking another device. This paper focuses on training a single neural network using traces from multiple EM probe positions to detect leakage from a larger area over the attacked device. We provide dual evaluation of EM traces - from two completely independent labs - profiling on data from one lab and attacking traces from the other lab.