miscellaneous-11

Cryptanalysis

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Encoding Cryptographic Functions to SAT Using TRANSALG System

I. Otpuschennikov, A. Semenov, I. Gribanova, O. Zaikin, S. Kochemazov•Sun Jul 03 2016

The technology for constructing propositional encodings of discrete functions aimed at solving inversion problems of considered functions using state-of-the-art SAT solvers is implemented, and the functionality of Transalg is compared in detail with that of similar software systems.

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Learning the Enigma with Recurrent Neural Networks

S. Greydanus•Wed Aug 23 2017

The results indicate that RNNs can learn algorithmic representations of black box polyalphabetic ciphers and that these representations are useful for cryptanalysis.

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Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning

A. Gohr•Sat Aug 17 2019

A highly selective key search policy based on a variant of Bayesian optimization which, together with the neural distinguishers, can be used to reduce the remaining security of 11-round Speck32/64 to roughly 38 bits.

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Improved (Related-Key) Differential-Based Neural Distinguishers for SIMON and SIMECK Block Ciphers

Jinyu Lu, Guoqiang Liu, Bing Sun, Chao Li, Li Liu•Mon Jan 10 2022

In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher SPECK32/64, achieving higher accuracy than the pure differential distinguishers. By its very nature, mining effective features in data plays a crucial role in data-driven deep learning. In this paper, in addition to considering the integrity of the information from the training data of the ciphertext pair, domain knowledge about the structure of differential cryptanalysis is also considered into the training process of deep learning to improve the performance. Meanwhile, taking the performance of the differential-neural distinguisher of SIMON32/64 as an entry point, we investigate the impact of input difference on the performance of the hybrid distinguishers to choose the proper input difference. Eventually, we improve the accuracy of the neural distinguishers of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128. We also obtain related-key differential-based neural distinguishers on round-reduced versions of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128 for the first time.

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