Improving the Selection Rule of Correlation Attacks for Remote Power Analysis

Remote power analysis is a novel threat to information systems. Under this attack model, the adversary does not require direct physical access to the platform or specialized sensing equipment. Most of the literature in this field deals with advanced acquisition methods and adversarial models. In contrast, side-channel analysis techniques for remote attacks have not been sufficiently explored. We bridge this gap by taking a look at the characteristics of the data recovered from remote power analysis. We use these insights to propose a novel selection rule for correlationbased attacks that boosts success confidence. This improvement comes from the observation that the samples in a power trace are not independent. We show that adjacent samples can also provide useful information by proposing a post-processing step that capitalizes on these additional leakages. In contrast to previous work, the proposed technique does not rely on the selection of points of interest within the power traces. We further investigate the characteristics of “remote” power traces and their effect on the proposed selection rule through experiments with real (TDC, ChipWhisperer) and synthetic data sets. To assess the advantage of the proposed improvement, we also introduce novel performance metrics that divert from known-key evaluation techniques.