Enhancing the Identification of Related-Key Neural Differential Distinguishers for SPECK32/64

Lightweight encryption algorithms play a vital role in securing communica-tions for resource constrained devices. As a prominent lightweight cipher, SPECK has attracted extensive security analyses. At ASIACRYPT 2023, Bao et al. introduced a related key neural network differential distinguisher capa-ble of effectively distinguishing 9 round SPECK32 64 ciphertexts and inte-grated it into a 1_s_r_1 key recovery framework to attack 14 round SPECK32 64. Inspired by their work, this paper presents a new related key neural differential distinguisher for SPECK32 64, built upon a novel relat-ed key processing method and an alternative network architecture, which significantly boosts the accuracy of distinguishing 10 round ciphertexts. Within the same 1_s_r_1 key recovery framework, we employed our trained distinguisher to recover the key of 15 round SPECK32 64. The spe-cific contributions are as follows: First, this paper introduces a novel related-key processing method, generating correlated subkey pairs for encrypting samples containing 64 plaintext pairs. Second, a related-key neural differen-tial distinguisher was constructed based on the Inception module from Goog-leNet and the DenseNet architecture. Experimental results demonstrate that the trained distinguisher achieves a recognition accuracy of 97.24 for 10-round ciphertexts, surpassing Bao et al.'s results by extending the recogniza-ble rounds by one. Finally, leveraging the 10-round neural distinguisher, this paper successfully executed a key recovery attack on 15-round SPECK32 64. Analysis of error-bit distributions revealed a correct key re-covery success rate of 98.67 .