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Improved Differential-ML Distinguisher: Machine Learning Based Generic Extension for Differential Analysis

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Information and Communications Security (ICICS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12919))

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Abstract

At CRYPTO 2019, Gohr first proposes a deep learning based differential analysis on round-reduced Speck32/64. Then Yadav \(et \, al.\) present a framework to construct the differential-ML (machine learning) distinguisher by combining the traditional differential distinguisher and the machine learning based differential distinguisher, which breaks the limit of the ML differential distinguisher on the number of attack rounds. However, the results obtained based on this method are not necessarily better than the results gained by traditional analysis. In this paper, we offer three novel greedy strategies (\(M_1\), \(M_2\) and \(M_3\)) to solve this problem. The strategy \(M_1\) provides better differential-ML distinguishers by considering all combinations of classical differential distinguishers and ML differential distinguishers. And the strategy \(M_2\) uses the best ML differential distinguishers to splice classical differential distinguishers forward, while the strategy \(M_3\) adopts the best classical differential distinguishers to splice ML differential distinguishers. As proof of works, we apply our methods to round-reduced Speck32/64, Speck48/72 and Speck64/96 and get some improved cryptanalysis results. For the construction of differential-ML distinguishers, we can reach 11-round Speck32/64, 14-round Speck48/72 and 18-round Speck64/96 with \(2^{27}\), \(2^{45}\), \(2^{62}\) data respectively.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China (No. 62072181), the National Cryptography Development Fund (No. MMJJ20180201), the International Science and Technology Cooperation Projects (No. 61961146004).

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, 200062, China

    Gao Wang & Gaoli Wang

  2. State Key Laboratory of Cryptology, P.O. Box 5159, Beijing, 100878, China

    Gaoli Wang

Authors
  1. Gao Wang
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  2. Gaoli Wang
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Corresponding author

Correspondence to Gaoli Wang .

Editor information

Editors and Affiliations

  1. Singapore Management University, Singapore, Singapore

    Debin Gao

  2. Tsinghua University, Beijing, China

    Qi Li

  3. Xi'an Jiaotong University, Xi'an, China

    Xiaohong Guan

  4. Chongqing University, Chongqing, China

    Xiaofeng Liao

Appendices

A The Best Differential Trails for Speck

Table 4. The best differential trails for Speck
Full size table

B The Partial Results for Sect. 5

Table 5. The partial results of the strategy \(M_2\) for Speck
Full size table
Table 6. The partial results of the strategy \(M_3\) for Speck
Full size table

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Wang, G., Wang, G. (2021). Improved Differential-ML Distinguisher: Machine Learning Based Generic Extension for Differential Analysis. In: Gao, D., Li, Q., Guan, X., Liao, X. (eds) Information and Communications Security. ICICS 2021. Lecture Notes in Computer Science(), vol 12919. Springer, Cham. https://doi.org/10.1007/978-3-030-88052-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-88052-1_2

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