Abstract
Differential cryptanalysis has proven to be a powerful tool to identify weaknesses in symmetric-key cryptographic systems such as block ciphers. Recent advances have shown that machine learning methods are able to produce very strong distinguishers for certain cryptographic systems. This has generated a large interest in the topic of machine learning for differential cryptanalysis as evidenced by a growing body of work in the last few years. In this paper we aim to provide a guide to the current state of the art in this topic in the hope that a unified view can better highlight the challenges and opportunities for researchers joining the field.
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Martínez, I., López, V., Rambaut, D., Obando, G., Gauthier-Umaña, V., Pérez, J.F. (2024). Recent Advances in Machine Learning for Differential Cryptanalysis. In: Tabares, M., Vallejo, P., Suarez, B., Suarez, M., Ruiz, O., Aguilar, J. (eds) Advances in Computing. CCC 2023. Communications in Computer and Information Science, vol 1924. Springer, Cham. https://doi.org/10.1007/978-3-031-47372-2_5
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