Performance comparison between deep learning-based and conventional cryptographic distinguishers

While many similarities between Machine Learning and cryptanalysis tasks exists, so far no major result in cryptanalysis has been reached with the aid of Machine Learning techniques. One exception is the recent work of Gohr, presented at Crypto 2019, where for the first time, conventional cryptanalysis was combined with the use of neural networks to build a more efficient distinguisher and, consequently, a key recovery attack on Speck32/64. On the same line, in this work we propose two Deep Learning (DL) based distinguishers against the Tiny Encryption Algorithm (TEA) and its evolution RAIDEN. Both ciphers have twice block and key size compared to Speck32/64. We show how these two distinguishers outperform a conventional statistical distinguisher, with no prior information on the cipher, and a differential distinguisher based on the differential trails presented by Biryukov and Velichkov at FSE 2014. We also present some variations of the DL-based distinguishers, discuss some of their extra features, and propose some directions for future research