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Deep Learning Assisted Differential Cryptanalysis for the Lightweight Cipher SIMON
2021
KSII Transactions on Internet and Information Systems
SIMON and SPECK are two families of lightweight block ciphers that have excellent performance on hardware and software platforms. At CRYPTO 2019, Gohr first introduces the differential cryptanalysis based deep learning on round-reduced SPECK32/64, and finally reduces the remaining security of 11-round SPECK32/64 to roughly 38 bits. In this paper, we are committed to evaluating the safety of SIMON cipher under the neural differential cryptanalysis. We firstly prove theoretically that SIMON is a
doi:10.3837/tiis.2021.02.012
fatcat:6ivrb3nmabau5cqv7b4moamy6q