Automatic hyperparameter tuning with hyperband for side-channel analysis in deep learning

Deep learning methods provide strong support for side channel analysis, and a large number of research results prove the advantages of this method in the field of side channel applications. Using deep learning for side-channel analysis offers advantages that traditional methods lack, such as its ability to counter certain protective measures like masking, and it doesn't require complex feature extraction processes. These advantages have made deep learning methods the primary tool for side-channel analysis. However, this does not mean that the use of the method is without drawbacks. One of the biggest difficulties is to find the appropriate hyperparameters for the neural network to bring out the best performance of the side channel analysis. In this paper, we propose an automatic hyperparameter tuning method for deep learning based on Hyperband in the field of side-channel analysis. This method can speed up the hyperparameter search by adaptive resource allocation. Experiments show that regardless of the type of leakage model and neural network, the hyperparameter optimization scheme in this paper performs well. Compared with Bayesian optimization and random search, the proposed scheme has better tuning performance and saves more than half of the tuning time.