International Association for Cryptologic Research (IACR)
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