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This paper investigates training a single neural network for side-channel analysis using electromagnetic (EM) traces acquired from multiple probe positions, aiming to improve the resilience of profiling attacks against variations in probe placement. They demonstrate that a network trained on data from multiple probe positions generalizes better across different labs and devices compared to networks trained on single positions. The dual evaluation, profiling on data from one lab and attacking traces from another, validates the approach's effectiveness in real-world scenarios.
Training a single neural network on EM traces from multiple probe positions makes side-channel attacks significantly more resilient to variations in probe placement and device characteristics.
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.
DeepMind