Neural Distinguishers on $\texttt {TinyJAMBU-128}$ and $\texttt {GIFT-64}$

Abstract

In CRYPTO 2019, Gohr first introduced a pioneering attempt, and successfully applied neural differential distinguisher ($\mathcal {NDD}$) based differential cryptanalysis against Speck32/64, achieving higher accuracy than the pure differential distinguishers and reducing the data complexity of chosen plaintexts. Inspired by Gohr’s work, we attempt to use neural network to analyze the cipher $\texttt {TinyJAMBU-128}$ which is one of ten NIST’s lightweight cryptography standardization process finalists. Based on MLP, we construct a Neural Single Differential Distinguisher ($\mathcal {NSDD}$), on which we get an accuracy of $99.58\%$ with 32-bit associated data(AD). The experiment results show that $\texttt {TinyJAMBU-128}$ with 32-bit AD is vulnerable to differential attacks. In this article, we also explore $\texttt {GIFT-64}$. Based on Long Short-Term Memory (LSTM), we construct $\mathcal {NSDD}$ and Neural Polytopic Differential Distinguisher($\mathcal {NPDD}$). For 4-,5-,6-round $\texttt {GIFT-64}$, we get an accuracy of $99.73\%, 85.08\%$, $57.54\%$ with $\mathcal {NPDD}$ and obtain an accuracy of $97.97\%, 75.11\%, 57.25\%$ with $\mathcal {NSDD}$ respectively. Compared with Yadav’s research in which MLP is used, we get a higher acccuracy with only $\frac{1}{4}$ train dataset. It shows that our model is better than Yadav’s.

’This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB2701602, the Xiangtan University scientific research project under Grant 15XZX32, the National Natural Science Foundation of China under Grant No. 62172350, the Hunan Province Department of Education under Grant 21B0120, the Natural Science Foundation of Hunan under Grant No. 2021JJ40544. The code is available at https://github.com/ASC8384/Neural-Distinguishers.