Profiling Resilient to Change in Probe Position
For side-channel analysis practitioners, this work addresses the practical challenge of probe positioning by enabling leakage detection without precise hot-spot alignment, though the improvement is incremental over existing augmentation methods.
This paper proposes training a single neural network on EM traces from multiple probe positions to detect side-channel leakage over a larger area, demonstrating cross-lab portability by profiling on data from one lab and attacking traces from another.
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.