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The absence of an algorithm that effectively monitors deep learning modelsused in side-channel attacks increases the difficulty of evaluation. If theattack is unsuccessful, the question is if we are dealing with a resistantimplementation or a faulty model. We propose an early stopping algorithm thatreliably recognizes the model's optimal state during training. The novelty ofour solution is an efficient implementation of guessing entropy estimation.Additionally, we formalize two conditions, persistence and patience, for a deeplearning model to be optimal. As a result, the model converges with fewertraces.

Being Patient and Persistent: Optimizing An Early Stopping Strategy for Deep Learning in Profiled Attacks