In recent years, ML based differential distinguishers have been explored and compared with the classical methods. Complexity of a key recovery attack on block ciphers is calculated using the probability of a differential distinguisher provided by classical methods. Since theoretical computations suffice to calculate the data complexity in these cases, so there seems no restrictions on the practical availability of computational resources to attack a block cipher using classical methods....

In a differential cryptanalysis attack, the attacker tries to observe a block cipher's behavior under an input difference: if the system's resulting output differences show any non-random behavior, a differential distinguisher is obtained. While differential cryptanlysis has been known for several decades, Gohr was the first to propose in 2019 the use of machine learning (ML) to build a distinguisher. In this paper, we present the first Partial Differential (PD) ML-distinguisher, and...

In CRYPTO 2019, Gohr shows that well-trained neural networks can perform cryptanalytic distinguishing tasks superior to traditional differential distinguishers. Moreover, applying an unorthodox key guessing strategy, an 11-round key-recovery attack on a modern block cipher Speck32/64 improves upon the published state-of-the-art result. This calls into the next questions. To what extent is the advantage of machine learning (ML) over traditional methods, and whether the advantage generally...

Differential cryptanalysis is an important technique to evaluate the security of block ciphers. There exists several generalisations of differential cryptanalysis and it is also used in combination with other cryptanalysis techniques to improve the attack complexity. In 2019, usefulness of machine learning in differential cryptanalysis is introduced by Gohr to attack the lightweight block cipher SPECK. In this paper, we present a framework to extend the classical differential distinguisher...