A New Neural Distinguisher Considering Features Derived From Multiple Ciphertext Pairs

Abstract

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.