Neural-Distinguishers

This repository holds supplementary code and data to the paper "Neural Distinguishers on TinyJAMBU-128 and GIFT-64".

Acknowledgement

1. Gohr A. (2019) Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning. In: Boldyreva A., Micciancio D. (eds) Advances in Cryptology – CRYPTO 2019. CRYPTO 2019. Lecture Notes in Computer Science, vol 11693. Springer, Cham. https://doi.org/10.1007/978-3-030-26951-7_6 (https://github.com/agohr/deep_speck)
2. Baksi A., Breier J., Dong X., Yi C.: Machine Learning Assisted DifferentialDistinguishers For Lightweight Ciphers. https://eprint.iacr.org/2020/571, (2020)
3. GIFT_64 differential characteristic can be verified using https://github.com/zhuby12/MILP-basedModel
4. Yadav T, Kumar M. Differential-ml distinguisher: Machine learning based generic extension for differential cryptanalysis[C]//International Conference on Cryptology and Information Security in Latin America. Springer, Cham, 2021: 191-212.https://link.springer.com/chapter/10.1007/978-3-030-88238-9_10 (https://github.com/tarunyadav/Differential-ML-Distinguisher)
5. Accelerated TinyJambu - Lightweight Authenticated Encryption Algorithms. https://github.com/itzmeanjan/tinyjambu.