Programming the Demirci-Selçuk Meet-in-the-Middle Attack with Constraints

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Abstract

Cryptanalysis with SAT/SMT, MILP and CP has increased in popularity among symmetric-key cryptanalysts and designers due to its high degree of automation. So far, this approach covers differential, linear, impossible differential, zero-correlation, and integral cryptanalysis. However, the Demirci-Selçuk meet-in-the-middle (DS-MITM) attack is one of the most sophisticated techniques that has not been automated with this approach. By an in-depth study of Derbez and Fouque’s work on (DS-MITM) analysis with dedicated search algorithms, we identify the crux of the problem and present a method for automatic (DS-MITM) attack based on general constraint programming, which allows the cryptanalysts to state the problem at a high level without having to say how it should be solved. Our method is not only able to enumerate distinguishers but can also partly automate the key-recovery process. This approach makes the (DS-MITM) cryptanalysis more straightforward and easier to follow, since the resolution of the problem is delegated to off-the-shelf constraint solvers and therefore decoupled from its formulation. We apply the method to SKINNY, TWINE, and LBlock, and we get the currently known best (DS-MITM) attacks on these ciphers. Moreover, to demonstrate the usefulness of our tool for the block cipher designers, we exhaustively evaluate the security of 40320 versions of LBlock instantiated with different words permutations in the F functions. It turns out that the permutation used in the original LBlock is one of the 64 permutations showing the strongest resistance against the (DS-MITM) attack. The whole process is accomplished on a PC in less than 2 h. The same process is applied to TWINE, and similar results are obtained.

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APA

Shi, D., Sun, S., Derbez, P., Todo, Y., Sun, B., & Hu, L. (2018). Programming the Demirci-Selçuk Meet-in-the-Middle Attack with Constraints. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11273 LNCS, pp. 3–34). Springer Verlag. https://doi.org/10.1007/978-3-030-03329-3_1

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