Cryptanalysis
6 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Encoding Cryptographic Functions to SAT Using Transalg System
We implemented this technology in the form of the software system called Transalg, and used it to construct SAT encodings for a number of cryptanalysis problems.
Learning the Enigma with Recurrent Neural Networks
We demonstrate that RNNs can learn decryption algorithms -- the mappings from plaintext to ciphertext -- for three polyalphabetic ciphers (Vigen\`ere, Autokey, and Enigma).
Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning
agohr/deep_speck
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Conference 2019
This paper has four main contributions. 1 First, we calculate the predicted difference distribution of Speck32/64 with one specific input difference under the Markov assumption completely for up to eight rounds and verify that this yields a globally fairly good model of the difference distribution of Speck32/64.
Improved (Related-key) Differential-based Neural Distinguishers for SIMON and SIMECK Block Ciphers
jin-smile/improved-related-key-differential-based-neural-distinguishers
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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.
Memorization for Good: Encryption with Autoregressive Language Models
Over-parameterized neural language models (LMs) can memorize and recite long sequences of training data.
Learning Quantum Processes with Quantum Statistical Queries
Learning complex quantum processes is a central challenge in many areas of quantum computing and quantum machine learning, with applications in quantum benchmarking, cryptanalysis, and variational quantum algorithms.
