International Association
for Cryptologic Research

Deep learning-based side-channel analysis is an extremely powerful option for profiling side-channel attacks. However, to perform well, one needs to select the neural network model and training time hyperparameters carefully. While many works investigated these aspects, random search could still be considered the current state-of-the-art. Unfortunately, random search has drawbacks, since the chances of finding a good architecture significantly drop when considering more complex targets. In this paper, we propose a novel neural architecture search approach for SCA based on grammatical evolution - SCAGE. We define a custom SCA grammar that allows us to find well-performing and potentially unconventional architectures. We conduct experiments on four datasets, considering both synchronized and desynchronized versions, as well as using feature intervals or raw traces. Our results show SCAGE to perform extremely well in all settings, outperforming random search and related works in most of the considered scenarios.