Deep Learning Based Cryptanalysis on SLIM Cipher

This paper provides an in-depth exploration of neural distinguishers in cryptanalysis with a focus on the SLIM Cipher. Building upon the work of Gohr, we aim to explain why neural distinguishers outperform traditional differential attacks and enhance contemporary methodologies. Our experiments centered on SLIM Cipher's fifth round and aimed to understand the influence of input difference, output difference, and previous round differentials on the accuracy of the neural distinguisher. Our investigations revealed that the choice of input difference was significant, and only focusing on maximizing the differential probability might not always yield the best results. Neural distinguisher appears to observe not just about the distribution of differences from the ciphertext but also additional cryptanalytic features or properties, which was a key insight from our research. Furthermore, we found that the output difference in the ciphertext played a vital role in the neural distinguisher's performance. By partially decrypting ciphertext pairs, we discovered that the output difference from the penultimate round was also a major factor impacting the accuracy of the neural distinguisher. This led to an improved methodology, yielding impressive accuracy results. These findings present valuable insights that can guide the future development and application of these tools in cryptanalysis.