ACNN: Arbitrary Traces Attack based on LeakageArea Detection

Abstract

Deep Learning-based Profiled Side-Channel Analysis (DL-PSCA) has become an important research method in the field of Side-Channel Analysis (SCA). The current research of DL-PSCA is based on publicly available datasets. Both the training set and attack set are derived from the same batch of collected data and are well-organized, resulting in the disregard of differences arising from the condition of trace measurement that exists in actual attack scenarios. Specifically, the timing of measurement is inconsistent due to the fact that attackers have different levels of control in terms of profiling and attacking devices. To bridge the gap, we assert that it is necessary for a network to explicitly identify areas of leakage and subsequently predict the leaked values. By decomposing conventional DL-PSCA into tasks of finer granularity, we have been able to restructure DL-PSCA as a regression task that operates in asliding window manner. Therefore, we present Arbitrary Convolutional Neural Network (ACNN) that is composed of a feature extractor and a regressor, which scans the input trace of arbitrary length for leakage localization and leakage value prediction in a sliding window way. We use the public datasets DPAv4.2 and ASCAD respectively to simulate variable traces and verify the effectiveness of our approach on unmasked and masked devices. The experiments show that no matter if the implementation is protected with masking scheme, the network can accurately identify the leakage areas and predict the leaked values. In terms of key recovery performance, our architecture is on par with state-of-the-art.