Paper 2021/066

A Deep Learning Approach for Active S-box Prediction of Lightweight Generalized Feistel Block Ciphers

Mohamed Fadl Idris, Je Sen Teh, Jasy Liew Suet Yan, and Wei-Zhu Yeoh


Block cipher resistance against differential cryptanalysis is commonly assessed by counting the number of active substitution boxes (S-boxes) using search algorithms or mathematical solvers that incur high computational costs. In this paper, we propose an alternative approach using deep neural networks to predict the number of active S-boxes, trading off exactness for real-time efficiency as the bulk of computational work is brought over to pre-processing (training). Active S-box prediction is framed as a regression task whereby neural networks are trained using features such as input and output differences, number of rounds, and permutation pattern. We first investigate the feasibility of the proposed approach by applying it on a reduced (4-branch) generalized Feistel structure (GFS) cipher. Apart from optimizing a neural network architecture for the task, we also explore the impact of each feature and its representation on prediction error. We then extend the idea to 64-bit GFS ciphers by first training neural networks using data from five different ciphers before using them to predict the number of active S-boxes for TWINE, a lightweight block cipher. The best performing model achieved the lowest root mean square error of 1.62 and R$^2$ of 0.87, depicting the feasibility of the proposed approach.

Note: This paper has been published in IEEE Access.

Available format(s)
Secret-key cryptography
Publication info
Published elsewhere. IEEE Access
Active s-boxesblock ciphercryptanalysisdeep learningdifferential cryptanalysislightweight cryptographyneural networksTWINE
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jesen_teh @ usm my
2021-07-29: last of 7 revisions
2021-01-18: received
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      author = {Mohamed Fadl Idris and Je Sen Teh and Jasy Liew Suet Yan and Wei-Zhu Yeoh},
      title = {A Deep Learning Approach for Active S-box Prediction of Lightweight Generalized Feistel Block Ciphers},
      howpublished = {Cryptology ePrint Archive, Paper 2021/066},
      year = {2021},
      doi = {10.1109/ACCESS.2021.3099802},
      note = {\url{}},
      url = {}
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