О возможности применения сверточных нейронных сетей к построению универсальных атак на итеративные блочные шифры

Прикладная дискретная математика. 2020. № 3. С. 46–56.

Perov A., Пестунов А. И.

The paper explores possibility of applying convolutional neural networks to the secu-
rity analysis of iterative block ciphers. A new approach for constructing distinguishing
attacks based on a convolutional neural network is proposed. The approach is based
on distinguishing between graphic equivalents of ciphertexts received by the CTR
(counter) encryption mode after different number of rounds, including the number
of rounds guaranteeing satisfaction of statistical properties. Several schemes are pre-
sented for constructing distinguishing attacks, which in some cases make it possible
to detect deviations from randomness in smaller samples than previously known, and
with a large number of rounds. The approach allows to create distinguishers without
the need for an analytical research of each cipher, which makes it possible to build
universal distinguishers for a series of ciphers

Keywords: машинное обучение, статистический анализ, нейронные сети, криптоанализ, neural networks, statistical analysis, machine learning, block cipher, блочный шифр, cryptanalysis, атака-различитель