Welcome to the resource topic for 2021/311
Title:
Improved Neural Aided Statistical Attack for Cryptanalysis
Authors: Yi Chen, Hongbo Yu
Abstract:At CRYPTO 2019, Gohr improved attacks on Speck32/64 using deep learning. In 2020, Chen et al. proposed a neural aided statistical attack that is more generic. Chen et’s attack is based on a statistical distinguisher that covers a prepended differential transition and a neural distinguisher. When the probability of the differential transition is pq, its impact on the data complexity is O(p^{-2}q^{-2}. In this paper, we propose an improved neural aided statistical attack based on a new concept named Homogeneous Set. Since partial random ciphertext pairs are filtered with the help of homogeneous sets, the differential transition’s impact on the data complexity is reduced to O(p^{−1}q^{−2}). As a demonstration, the improved neural aided statistical attack is applied to round-reduced Speck. And several better attacks are obtained.
ePrint: https://eprint.iacr.org/2021/311
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