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<p>In CRYPTO 2019, Gohr introduced machine learning-aided differential cryptanalysis, demonstrating superior performance in key-recovery attacks compared to traditional methods. This advancement has sparked significant interest in exploring the potential of machine learning for enhancing the effectiveness and efficiency of cryptanalysis. To address these phenomena, we develop an innovative framework that integrates machine learning into differential-linear cryptanalysis and apply it to the 8-round <span>Des</span>, which achieves better performance than classical methods. Additionally, considering the existence of the non-trivial differentials in differential-linear distinguishers, we employ the generalized neutral bits during the generating training data phase and the key-guessing phase to improve the accuracy of neural-aided differential-linear distinguishers of <span>Speck</span>. For comparison, we further perform the traditional key-recovery attacks, whose results show that machine learning-aided cryptanalysis has considerable advantages in success rate over attacks devised using pure counterparts. As the first machine learning-aided differential-linear cryptanalysis, our works not only extends the application of machine learning in cryptanalysis but also provides valuable insights into the potential of integrating deep learning for enhancing cryptanalysis.</p>

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Machine learning-aided differential-linear attacks with applications to Des and Speck32/64

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Ze-zhou Hou,
  • Jiong-jiong Ren,
  • Shao-zhen Chen

摘要

In CRYPTO 2019, Gohr introduced machine learning-aided differential cryptanalysis, demonstrating superior performance in key-recovery attacks compared to traditional methods. This advancement has sparked significant interest in exploring the potential of machine learning for enhancing the effectiveness and efficiency of cryptanalysis. To address these phenomena, we develop an innovative framework that integrates machine learning into differential-linear cryptanalysis and apply it to the 8-round Des, which achieves better performance than classical methods. Additionally, considering the existence of the non-trivial differentials in differential-linear distinguishers, we employ the generalized neutral bits during the generating training data phase and the key-guessing phase to improve the accuracy of neural-aided differential-linear distinguishers of Speck. For comparison, we further perform the traditional key-recovery attacks, whose results show that machine learning-aided cryptanalysis has considerable advantages in success rate over attacks devised using pure counterparts. As the first machine learning-aided differential-linear cryptanalysis, our works not only extends the application of machine learning in cryptanalysis but also provides valuable insights into the potential of integrating deep learning for enhancing cryptanalysis.