A Deep Neural Differential Distinguisher for ARX based Block Cipher

Over the last few years, deep learning is becoming the most trending topic for the classical cryptanalysis of block ciphers. Differential cryptanalysis is one of the primary and potent attacks on block ciphers. Here we apply deep learning techniques to model differential cryptanaly- sis more easily. In this paper, we report a generic tool called NDDT1, us- ing deep neural classifier that assists to find differential distinguishers for symmetric block ciphers with reduced round. We apply this approach for the differential cryptanalysis of ARX-based encryption schemes HIGHT, LEA, SPARX and SAND. To the best of our knowledge, this is the first deep learning-based distinguisher for the mentioned ciphers. The result shows that our deep learning based distinguishers work with high accuracy for 14-round HIGHT, 13-Round LEA, 11-round SPARX and 14-round SAND128. The relationship between the hamming weight of input difference of a neural distinguisher and the corresponding maxi- mum round number of the cipher has been justified through exhaustive experimentation. The lower bounds of data complexity for differential cryptanalysis have also been improved

A Survey of ARX-based Symmetric-key Primitives

Addition Rotation XOR is suitable for fast implementation symmetric –key primitives, such as stream and block ciphers. This paper presents a review of several block and stream ciphers based on ARX construction followed by the discussion on the security analysis of symmetric key primitives where the best attack for every cipher was carried out. We benchmark the implementation on software and hardware according to the evaluation metrics. Therefore, this paper aims at providing a reference for a better selection of ARX design strategy

A Bit-Vector Differential Model for the Modular Addition by a Constant

ARX algorithms are a class of symmetric-key algorithms constructed by Addition, Rotation, and XOR, which achieve the best software performances in low-end microcontrollers. To evaluate the resistance of an ARX cipher against differential cryptanalysis and its variants, the recent automated methods employ constraint satisfaction solvers, such as SMT solvers, to search for optimal characteristics. The main difficulty to formulate this search as a constraint satisfaction problem is obtaining the differential models of the non-linear operations, that is, the constraints describing the differential probability of each non-linear operation of the cipher. While an efficient bit-vector differential model was obtained for the modular addition with two variable inputs, no differential model for the modular addition by a constant has been proposed so far, preventing ARX ciphers including this operation from being evaluated with automated methods. In this paper, we present the first bit-vector differential model for the n-bit modular addition by a constant input. Our model contains O(log2(n)) basic bit-vector constraints and describes the binary logarithm of the differential probability. We also represent an SMT-based automated method to look for differential characteristics of ARX, including constant additions, and we provide an open-source tool ArxPy to find ARX differential characteristics in a fully automated way. To provide some examples, we have searched for related-key differential characteristics of TEA, XTEA, HIGHT, and LEA, obtaining better results than previous works. Our differential model and our automated tool allow cipher designers to select the best constant inputs for modular additions and cryptanalysts to evaluate the resistance of ARX ciphers against differential attacks.acceptedVersio

A Cipher-Agnostic Neural Training Pipeline with Automated Finding of Good Input Differences

Neural cryptanalysis is the study of cryptographic primitives through machine learning techniques. Following Gohr’s seminal paper at CRYPTO 2019, a focus has been placed on improving the accuracy of such distinguishers against specific primitives, using dedicated training schemes, in order to obtain better key recovery attacks based on machine learning. These distinguishers are highly specialized and not trivially applicable to other primitives. In this paper, we focus on the opposite problem: building a generic pipeline for neural cryptanalysis. Our tool is composed of two parts. The first part is an evolutionary algorithm for the search of good input differences for neural distinguishers. The second part is DBitNet, a neural distinguisher architecture agnostic to the structure of the cipher. We show that this fully automated pipeline is competitive with a highly specialized approach, in particular for SPECK32, and SIMON32. We provide new neural distinguishers for several primitives (XTEA, LEA, HIGHT, SIMON128, SPECK128) and improve over the state-of-the-art for PRESENT, KATAN, TEA and GIMLI

Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: a DES instance

Encryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential attack to the Data Encryption Standard (DES) which, despite being one of the methods that has been most thoroughly analyzed, is still of great interest to the scientific community since its vulnerabilities may have implications on other ciphers

Proposing an MILP-based method for the experimental verification of difference-based trails: application to SPECK, SIMECK

Under embargo until: 2022-07-08Searching for the right pairs of inputs in difference-based distinguishers is an important task for the experimental verification of the distinguishers in symmetric-key ciphers. In this paper, we develop an MILP-based approach to verify the possibility of difference-based distinguishers and extract the right pairs. We apply the proposed method to some published difference-based trails (Related-Key Differentials (RKD), Rotational-XOR (RX)) of block ciphers SIMECK, and SPECK. As a result, we show that some of the reported RX-trails of SIMECK and SPECK are incompatible, i.e. there are no right pairs that follow the expected propagation of the differences for the trail. Also, for compatible trails, the proposed approach can efficiently speed up the search process of finding the exact value of a weak key from the target weak key space. For example, in one of the reported 14-round RX trails of SPECK, the probability of a key pair to be a weak key is 2−94.91 when the whole key space is 296; our method can find a key pair for it in a comparatively short time. It is worth noting that it was impossible to find this key pair using a traditional search. As another result, we apply the proposed method to SPECK block cipher, to construct longer related-key differential trails of SPECK which we could reach 15, 16, 17, and 19 rounds for SPECK32/64, SPECK48/96, SPECK64/128, and SPECK128/256, respectively. It should be compared with the best previous results which are 12, 15, 15, and 20 rounds, respectively, that both attacks work for a certain weak key class. It should be also considered as an improvement over the reported result of rotational-XOR cryptanalysis on SPECK.acceptedVersio

A survey on machine learning applied to symmetric cryptanalysis

In this work we give a short review of the recent progresses of machine learning techniques applied to cryptanalysis of symmetric ciphers, with particular focus on artificial neural networks. We start with some terminology and basics of neural networks, to then classify the recent works in two categories: "black-box cryptanalysis", techniques that not require previous information about the cipher, and "neuro-aided cryptanalysis", techniques used to improve existing methods in cryptanalysis

Differential Cryptanalysis in ARX Ciphers with specific applications to LEA

In this paper we focus on differential cryptanalysis dedicated to a particular class of cryptographic algorithms, namely ARX ciphers. We propose a new algorithm inspired by the Nested Monte-Carlo Search algorithm to find a differential path in ARX ciphers. We apply our algorithm to a round reduced variant of the block cipher LEA. We use the concept of a partial difference distribution table (pDDT) in our algorithm to reduce the search space. This methodology reduced the search space of the algorithm by using only those differentials whose probabilities are greater than or equal to pre-defined threshold. Using this concept we removed many differentials which are not valid or whose probabilities are very low. By doing this we decreased the time of finding a differential path by our nested algorithm due to a smaller search space. This partial difference distribution table also made our nested algorithm suitable for bigger block size ARX ciphers. Finding long differential characteristics is one of the hardest problems where we have seen other algorithms take many hours or days to find differential characteristics in ARX ciphers. Our algorithm finds the differential characteristics in just a few minutes with a very simple framework. We report the differential path for up to 9 rounds in LEA. To construct differential characteristics for a large number of rounds, we divide long characteristics into short ones, by constructing a large characteristic from two short characteristics. Instead of starting from the first round, we start from the middle and run experiments in forward as well as in the reverse direction. Using this method we improved our results and report the differential path for up to 12 rounds. Overall, the best property of our algorithm is that it has potential to provide state-of-the-art results but within a simpler framework as well as less time. Our algorithm is also very interesting for future aspect of research, as it could be applied to other ARX ciphers with a very easy going framework