Abstract
Deep learning-based cryptanalysis is one of the emerging trends in recent times. Differential cryptanalysis is one of the most potent approaches to classical cryptanalysis. Researchers are now modeling classical differential cryptanalysis by applying deep learning-based techniques. In this paper, we report deep learning-based differential distinguishers for block cipher PRIDE and RC5, utilizing deep learning models: CNN, LGBM and LSTM. We found distinguishers up to 23 rounds for PRIDE and nine rounds for RC5. To the best of our knowledge this is the first deep learning based differential classifier for cipher PRIDE and RC5.
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Pal, D., Mandal, U., Das, A., Chowdhury, D.R. (2023). Deep Learning Based Differential Classifier of PRIDE and RC5. In: Prabhu, S., Pokhrel, S.R., Li, G. (eds) Applications and Techniques in Information Security . ATIS 2022. Communications in Computer and Information Science, vol 1804. Springer, Singapore. https://doi.org/10.1007/978-981-99-2264-2_4
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