InfoNEAT: Information Theory-based NeuroEvolution of Augmenting Topologies for Side-channel Analysis

Profiled side-channel analysis (SCA) leverages leakage from cryptographic implementations to extract the secret key. When combined with advanced methods in neural networks (NNs), profiled SCA can successfully attack even those crypto-cores assumed to be protected against SCA. Despite the rise in the number of studies devoted to NN-based SCA, existing methods could not systematically address the challenges involved in the NN-based SCA. A range of questions has remained unanswered, namely: how to choose a NN with an adequate size, how to tune the NN's hyperparameters, when to stop the training, and how to explain the performance of the NN model in quantitative terms, in the context of SCA. Our proposed approach, "InfoNEAT," tackles these issues in a natural way. InfoNEAT relies on the concept of evolution of NNs (both the network architecture and parameters, so-called neuroevolution), enhanced by information-theoretic metrics to guide the evolution, halt it with a novel stopping criteria, and improve time-complexity and memory footprint. The performance of InfoNEAT is evaluated by applying it to publicly available datasets composed of real side-channel measurements. In addition to the considerable advantages regarding the automated configuration of NNs, InfoNEAT demonstrates significant improvements over other approaches including a reduction in the number of epochs and width of the NN (i.e., the number of nodes in a layer) by factors of at least 1.25 and 6.66, respectively. According to our assessment and on the basis of our results, this is indeed achieved without any deterioration in the performance of SCA compared to the state-of-the-art NN-based methods.