Machine Learning-Assisted Differential Distinguishers for Lightweight Ciphers

Abstract

At CRYPTO’19, Gohr first introduces the deep learning-based cryptanalysis on round-reduced SPECK. Using a deep residual network, Gohr trains several neural network-based distinguishers on 8-round SPECK -32/64. The analysis follows an “all-in-one” differential cryptanalysis approach, which considers all the output differences effect under the same input difference. Usually, the all-in-one differential cryptanalysis is more effective compared to the one using only one single differential trail. However, when the cipher is non-Markov or its block size is large, it is usually very hard to fully compute. Inspired by Gohr’s work, we try to simulate the all-in-one differentials for non-Markov ciphers through machine learning. Our idea here is to reduce a distinguishing problem to a classification problem so that it can be efficiently managed by machine learning. As a proof of concept, we show several distinguishers for four high-profile ciphers, each of which works with trivial complexity. In particular, we show differential distinguishers for 8-round GIMLI-HASH, GIMLI-CIPHER and GIMLI-PERMUTATION; 3-round ASCON-PERMUTATION; 10-round KNOT-256 permutation and 12-round KNOT-512 permutation; and 4-round CHASKEY- PERMUTATION. Finally, we explore more on choosing an efficient machine learning model and observe that only a three-layer neural network can be used. Our analysis shows the attacker is able to reduce the complexity of finding distinguishers by using machine learning techniques.