New Impossible Differential Characteristic of SPECK64 using MILP

Impossible differential attack is one of powerful methods for analyzing block ciphers. When designing block ciphers, it must be safe for impossible differential attacks. In case of impossible differential attack, the attack starts from finding the impossible differential characteristic. However, in the case of the ARX-based block cipher, these analyzes were difficult due to the addition of modulus. In this paper, we introduce 157 new six-round impossible differential characteristics of ARX-basef block cipher, SPECK64, using Mixed Integer Linear Programming (MILP) base impossible differential characteristic search proposed by Cui [3] etc

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

MILP-Based Automatic Search Algorithms for Differential and Linear Trails for Speck

In recent years, Mixed Integer Linear Programming (MILP) has been successfully applied in searching for differential characteristics and linear approximations in block ciphers and has produced the significant results for some ciphers such as SIMON (a family of lightweight and hardware-optimized block ciphers designed by NSA) etc. However, in the literature, the MILP-based automatic search algorithm for differential characteristics and linear approximations is still infeasible for block ciphers such as ARX constructions. In this paper, we propose an MILP-based method for automatic search for differential characteristics and linear approximations in ARX ciphers. By researching the properties of differential characteristic and linear approximation of modular addition in ARX ciphers, we present a method to describe the differential characteristic and linear approximation with linear inequalities under the assumptions of independent inputs to the modular addition and independent rounds. We use this representation as an input to the publicly available MILP optimizer Gurobi to search for differential characteristics and linear approximations for ARX ciphers. As an illustration, we apply our method to Speck, a family of lightweight and software-optimized block ciphers designed by NSA, which results in the improved differential characteristics and linear approximations compared with the existing ones. Moreover, we provide the improved differential attacks on Speck48, Speck64, Speck96 and Speck128, which are the best attacks on them in terms of the number of rounds

Proposing an MILP-based Method for the Experimental Verification of Difference Trails

Search 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 presented difference-based trails (Related-Key Differentials (RKD), Rotational-XOR (RX)) of block ciphers \texttt{SIMECK}, and \texttt{SPECK}. As a result, we show that some of the reported RX-trails of \texttt{SIMECK} and \texttt{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 \texttt{SPECK}, the probability of a key pair to be a weak-key is $2^{-94.91}$ when the whole key space is $2^{96}$; 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 %and consider a search strategy for the framework of to \texttt{SPECK} block cipher, to construct longer related-key differential trails of \texttt{SPECK} which we could reach 15, 16, 17, and 19 rounds for \texttt{SPECK32/64}, \texttt{SPECK48/96}, \texttt{SPECK64/128}, and \texttt{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 \texttt{SPECK}

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

Differential-Linear Approximation Semi-Unconstrained Searching and Partition Tree: Application to LEA and Speck

The differential-linear attack is one of the most effective attacks against ARX ciphers. However, two technical problems are preventing it from being more effective and having more applications: (1) there is no efficient method to search for good differential-linear approximations. Existing methods either have many constraints or are currently inefficient. (2) partitioning technique has great potential to reduce the time complexity of the key-recovery attack, but there is no general tool to construct partitions for ARX ciphers. In this work, we step forward in solving the two problems. First, we propose a novel idea for generating new good differential-linear approximations from known ones, based on which new searching algorithms are designed. Second, we propose a general tool named partition tree, for constructing partitions for ARX ciphers. Based on these new techniques, we present better attacks for two ISO/IEC standards, i.e., LEA and Speck. For LEA, we present the first 17-round distinguisher which is 1 round longer than the previous best distinguisher. Furthermore, we present the first key recovery attacks on 17-round LEA-128, 18-round LEA-192, and 18-round LEA-256, which attack 3, 4, and 3 rounds more than the previous best attacks. For Speck, we find better differential-linear distinguishers for Speck48 and Speck64. The first differential-linear distinguishers for Speck96 and Speck128 are also presented

CLAASP: a Cryptographic Library for the Automated Analysis of Symmetric Primitives

This paper introduces CLAASP, a Cryptographic Library for the Automated Analysis of Symmetric Primitives. The library is designed to be modular, extendable, easy to use, generic, efficient and fully automated. It is an extensive toolbox gathering state-of-the-art techniques aimed at simplifying the manual tasks of symmetric primitive designers and analysts. CLAASP is built on top of Sagemath and is open-source under the GPLv3 license. The central input of CLAASP is the description of a cryptographic primitive as a list of connected components in the form of a directed acyclic graph. From this representation, the library can automatically: (1) generate the Python or C code of the primitive evaluation function, (2) execute a wide range of statistical and avalanche tests on the primitive, (3) generate SAT, SMT, CP and MILP models to search, for example, differential and linear trails, (4) measure algebraic properties of the primitive, (5) test neural-based distinguishers. In this work, we also present a comprehensive survey and comparison of other software libraries aiming at similar goals as CLAASP

Automated Truncation of Differential Trails and Trail Clustering in ARX

We propose a tool for automated truncation of differential trails in ciphers using modular addition, bitwise rotation, and XOR (ARX). The tool takes as input a differential trail and produces as output a set of truncated differential trails. The set represents all possible truncations of the input trail according to certain predefined rules. A linear-time algorithm for the exact computation of the differential probability of a truncated trail that follows the truncation rules is proposed. We further describe a method to merge the set of truncated trails into a compact set of non-overlapping truncated trails with associated probability and we demonstrate the application of the tool on block cipher Speck64. We have also investigated the effect of clustering of differential trails around a fixed input trail. The best cluster that we have found for 15 rounds has probability 2^−55.03 (consisting of 389 unique output differences) which allows us to build a distinguisher using 128 times less data than the one based on just the single best trail, which has probability 2^−62. Moreover, we show examples for Speck64 where a cluster of trails around a suboptimal (in terms of probability) input trail results in higher overall probability compared to a cluster obtained around the best differential trail

A new method for Searching Optimal Differential and Linear Trails in ARX Ciphers

In this paper, we propose an automatic tool to search for optimal differential and linear trails in ARX ciphers. It\u27s shown that a modulo addition can be divided into sequential small modulo additions with carry bit, which turns an ARX cipher into an S-box-like cipher. From this insight, we introduce the concepts of carry-bit-dependent difference distribution table (CDDT) and carry-bit-dependent linear approximation table (CLAT). Based on them, we give efficient methods to trace all possible output differences and linear masks of a big modulo addition, with returning their differential probabilities and linear correlations simultaneously. Then an adapted Matsui\u27s algorithm is introduced, which can find the optimal differential and linear trails in ARX ciphers. Besides, the superiority of our tool\u27s potency is also confirmed by experimental results for round-reduced versions of HIGHT and SPECK. More specifically, we find the optimal differential trails for up to 10 rounds of HIGHT, reported for the first time. We also find the optimal differential trails for 10, 12, 16, 8 and 8 rounds of SPECK32/48/64/96/128, and report the provably optimal differential trails for SPECK48 and SPECK64 for the first time. The optimal linear trails for up to 9 rounds of HIGHT are reported for the first time, and the optimal linear trails for 22, 13, 15, 9 and 9 rounds of SPECK32/48/64/96/128 are also found respectively. These results evaluate the security of HIGHT and SPECK against differential and linear cryptanalysis. Also, our tool is useful to estimate the security in the design of ARX ciphers

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