Abstract
In CRYPTO 2019, Gohr uses the residual network technology of artificial intelligence to build a differential distinguisher, and attacks the reduced-round Speck32/64. We tried this method to recover the keys for ten-round Simon32/64. In this paper, we have three innovations. First, we construct polytope neural network distinguisher. On eight-round Simon32/64, polytope neural network distinguisher could increase the success rate of three neural network distinguishers with 0.76 success rate to 0.92. Second, we propose an attack on Simon32/64 based on the combination of the probability of differential path and polytope neural network distinguisher. This method can only increase the computational complexity of the chosen data as the number of rounds increases. Nine-round polytope neural network distinguisher is used to filter out data, whether it is what we want. Eight-round neural distinguisher is used to recover the final round key. The computational complexity of key recovery on the final key of eleven-round Simon32/64 is \(2^{33.4}\). Third, we propose an attack called Bayesian Key Research with Error. With this attack, the computational complexity of key recovery on the final key of eleven-round Simon32/64 is \(2^{30.9}\).
In our paper, the main idea is combining polytope differences with neural networks. By constructing polytope differential neural network distinguisher, we make a key recovery attack. In order to increase the number of rounds, we first used brute force attack and then proposed Bayesian Key Research with Error. We think this idea can be applied to many cryptographic algorithms.
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Su, HC., Zhu, XY., Ming, D. (2021). Polytopic Attack on Round-Reduced Simon32/64 Using Deep Learning. In: Wu, Y., Yung, M. (eds) Information Security and Cryptology. Inscrypt 2020. Lecture Notes in Computer Science(), vol 12612. Springer, Cham. https://doi.org/10.1007/978-3-030-71852-7_1
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