Print Email Facebook Twitter The Need for Speed Title The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA Author Perin, G. (Universiteit Leiden) Wu, L. (TU Delft Cyber Security) Picek, S. (Radboud Universiteit Nijmegen) Date 2023 Abstract The adoption of deep neural networks for profiling side-channel attacks opened new perspectives for leakage detection. Recent publications showed that cryptographic implementations featuring different countermeasures could be broken without feature selection or trace preprocessing. This success comes with a high price: an extensive hyperparameter search to find optimal deep learning models. As deep learning models usually suffer from overfitting due to their high fitting capacity, it is crucial to avoid over-training regimes, which require a correct number of epochs. For that, early stopping is employed as an efficient regularization method that requires a consistent validation metric. Although guessing entropy is a highly informative metric for profiling side-channel attacks, it is time-consuming, especially if computed for all epochs during training, and the number of validation traces is significantly large. This paper shows that guessing entropy can be efficiently computed during training by reducing the number of validation traces without affecting the efficiency of early stopping decisions. Our solution significantly speeds up the process, impacting the performance of the hyperparameter search and overall profiling attack. Our fast guessing entropy calculation is up to 16× faster, resulting in more hyperparameter tuning experiments and allowing security evaluators to find more efficient deep learning models. Subject deep learningfast guessing entropyguessing entropyside-channel attacksvalidation phase To reference this document use: http://resolver.tudelft.nl/uuid:1f0c99bc-8cca-47ae-ae3d-abe9d906308f DOI https://doi.org/10.3390/a16030127 ISSN 1999-4893 Source Algorithms, 16 (3) Part of collection Institutional Repository Document type journal article Rights © 2023 G. Perin, L. Wu, S. Picek Files PDF algorithms_16_00127.pdf 2.09 MB Close viewer /islandora/object/uuid:1f0c99bc-8cca-47ae-ae3d-abe9d906308f/datastream/OBJ/view