Abstract In Crypto’19, Gohr proposed the first deep learning-based key recovery attack on 11-round Speck32/64, which opens the direction of neural-aided cryptanalysis. Until now, neural-aided cryptanalysis still faces two problems: (i) the attack complexity estimations rely purely on practical experiments; (ii) it does not work when there are not enough neutral bits. To the best of our knowledge, we are the first to solve these two problems. In this paper, we propose a Neural-Aided Statistical Attack (NASA) that has the following advantages: (i) NASA supports estimating the theoretical complexity. (ii) NASA does not rely on any special properties including neutral bits. Moreover, we propose three methods for reducing the complexity of NASA. One of the methods, which is based on a newly proposed concept named Informative Bit that reveals an important phenomenon, makes NASA applicable to large-size ciphers. We have performed a series of experiments on round reduced Speck32/64, DES, and Speck96/96. These experiments do not only verify the correctness of NASA, but also further highlight the advantage and potential of NASA. Our work arguably raises a new direction for neural-aided cryptanalysis.

Abstract Neural-aided cryptanalysis is a challenging topic, in which the neural distinguisher ($\mathcal{ND}$) is a core module. In this paper, we propose a new $\mathcal{ND}$ considering multiple ciphertext pairs simultaneously. Besides, multiple ciphertext pairs are constructed from different keys. The motivation is that the distinguishing accuracy can be improved by exploiting features derived from multiple ciphertext pairs. To verify this motivation, we have applied this new $\mathcal{ND}$ to five different ciphers. Experiments show that taking multiple ciphertext pairs as input indeed brings accuracy improvement. Then, we prove that our new $\mathcal{ND}$ applies to two different neural-aided key recovery attacks. Moreover, the accuracy improvement is helpful for reducing the data complexity of the neural-aided statistic attack. The code is available at https://github.com/AI-Lab-Y/ND_mc.