Trace augmentation: What can be done even before preprocessing in a profiled SCA?

Preprocessing is an important first step in side-channel attacks, especially for template attacks. Typical processing techniques, such as Principal Component Analysis (PCA) and Singular Spectrum Analysis (SSA), mainly aim to reduce noise and/or extract useful information from raw data, and they are barely robust to tolerate differences between profiling and target traces. In this paper, we propose an efficient and easy-to-implement approach to preprocessing by applying the data augmentation method from deep learning, whose appropriate parameters can be efficiently determined using a simple validation. Our trace augmentation method, when added prior to existing profiling methods, significantly enhances robustness and improves performance of the attacks. Simulation-based experiments show that our approach not only results in a more robust profiling (even show an enhancement to the known robust profilings), but also works well in the ideal scenario (no distortions between profiling and target traces). The results of FPGA-based and software experiments are consistent to the ones of simulation-based counterparts. Thus, we conclude that the proposed augmentation method is an efficient performance-boosting add-on to profiled side-channel attacks in real world.