Truncated Differential-Neural Key Recovery Attacks on Round-Reduced HIGHT

Abstract

Recently, differential-neural cryptanalysis, which integrates deep learning with differential cryptanalysis, has emerged as a powerful and practical cryptanalysis method. It has been particularly applied to lightweight block ciphers, which are characterized by simple structures and operations, and relatively small block and key sizes. In resource-constrained environments, such as Internet of Things (IoT), it is essential to verify the resistance of existing lightweight block ciphers against differential-neural cryptanalysis to ensure security. In differential-neural cryptanalysis, a deep learning model, known as a neural distinguisher, is trained to differentiate a target cipher from others, facilitating key recovery through statistical analysis. For successful differential-neural cryptanalysis, it is crucial to develop a highly accurate neural distinguisher and to optimize the key recovery attack algorithm. In this paper, we introduce a novel neural distinguisher and key recovery attack against the 15-round reduced HIGHT cipher. Our proposed neural distinguisher is capable of distinguishing HIGHT ciphertext by analyzing only a portion of the ciphertext, which we refer to as a truncated neural distinguisher. Notably, our experiments demonstrate that the truncated neural distinguisher achieves performance comparable to existing distinguishers trained on entire ciphertext blocks, while enabling a more efficient key recovery attack through a divide-and-conquer strategy. Furthermore, we observe a significant improvement in key recovery efficiency compared to traditional cryptanalysis methods.