Differential cryptanalysis

About: Differential cryptanalysis is a research topic. Over the lifetime, 2131 publications have been published within this topic receiving 54681 citations.

µ 2 : A Lightweight Block Cipher

Abstract: This paper presents a 64-bit lightweight block cipher, µ2 with a key size of 80-bit. µ2 is designed based on well-established design paradigms, achieving comparable performance and security when compared against existing state-of-the-art lightweight block ciphers. µ2 is based on the Type-II generalized Feistel structure with a round function, F that is a 16-bit ultra-lightweight block cipher based on the substitution-permutation network. Security evaluation indicates that µ2 offers a large security margin against known attacks such as differential cryptanalysis, linear cryptanalysis, algebraic attack and others.

Differential and rectangle attacks on reduced-round SHACAL-1

Abstract: SHACAL-1 is an 80-round block cipher with a 160-bit block size and a key of up to 512 bits. In this paper, we mount rectangle attacks on the first 51 rounds and a series of inner 52 rounds of SHACAL-1, and also mount differential attacks on the first 49 rounds and a series of inner 55 rounds of SHACAL-1. These are the best currently known cryptanalytic results on SHACAL-1 in an one key attack scenario.

The block cipher SC2000

Abstract: In this paper, we propose a new symmetric key block cipher SC2000 with 128-bit block length and 128-,192-,256-bit key lengths The block cipher is constructed by piling two layers: one is a Feistel structure layer and the other is an SPN structure layer Each operation used in two layers is S-box or logical operation, which has been well studied about security It is a strong feature of the cipher that the fast software implementations are available by using the techniques of putting together S-boxes in various ways and of the Bitslice implementation

Machine Learning Assisted Differential Distinguishers For Lightweight Ciphers

Abstract: At CRYPTO 2019, 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.