Generating graphs packed with paths: Estimation of linear approximations and differentials:Estimation of linear approximations and differentials

When designing a new symmetric-key primitive, the designer must show resistance to known attacks. Perhaps most prominent amongst these are linear and differential cryptanalysis. However, it is notoriously difficult to accurately demonstrate e.g. a block cipher’s resistance to these attacks, and thus most designers resort to deriving bounds on the linear correlations and differential probabilities of their design. On the other side of the spectrum, the cryptanalyst is interested in accurately assessing the strength of a linear or differential attack. While several tools have been developed to search for optimal linear and differential trails, e.g. MILP and SAT based methods, only few approaches specifically try to find as many trails of a single approximation or differential as possible. This can result in an overestimate of a cipher’s resistance to linear and differential attacks, as was for example the case for PRESENT. In this work, we present a new algorithm for linear and differential trail search. The algorithm represents the problem of estimating approximations and differentials as the problem of finding many long paths through a multistage graph. We demonstrate that this approach allows us to find a very large number of good trails for each approximation or differential. Moreover, we show how the algorithm can be used to efficiently estimate the key dependent correlation distribution of a linear approximation, facilitating advanced linear attacks. We apply the algorithm to 17 different ciphers, and present new and improved results on several of these

Full-Round Differential Attack on ULC and LICID Block Ciphers Designed for IoT

The lightweight block ciphers ULC and LICID are introduced by Sliman et al. (2021) and Omrani et al. (2019) respectively. These ciphers are based on substitution permutation network structure. ULC is designed using the ULM method to increase efficiency, memory usage, and security. On the other hand, LICID is specifically designed for image data. In the ULC paper, the authors have given a full-round differential characteristic with a probability of $2^{-80}$. In the LICID paper, the authors have presented an 8-round differential characteristic with a probability of $2^{-112.66}$. In this paper, we present the 15-round ULC and the 14-round LICID differential characteristics of probabilities $2^{-45}$ and $2^{-40}$ respectively using the MILP model

Generating Graphs Packed with Paths

When designing a new symmetric-key primitive, the designer must show resistance to known attacks. Perhaps most prominent amongst these are linear and differential cryptanalysis. However, it is notoriously difficult to accurately demonstrate e.g. a block cipher\u27s resistance to these attacks, and thus most designers resort to deriving bounds on the linear correlations and differential probabilities of their design. On the other side of the spectrum, the cryptanalyst is interested in accurately assessing the strength of a linear or differential attack. While several tools have been developed to search for optimal linear and differential trails, e.g. MILP and SAT based methods, only few approaches specifically try to find as many trails of a single approximation or differential as possible. This can result in an overestimate of a cipher\u27s resistance to linear and differential attacks, as was for example the case for PRESENT. In this work, we present a new algorithm for linear and differential trail search. The algorithm represents the problem of estimating approximations and differentials as the problem of finding many long paths through a multistage graph. We demonstrate that this approach allows us to find a very large number of good trails for each approximation or differential. Moreover, we show how the algorithm can be used to efficiently estimate the key dependent correlation distribution of a linear approximation, facilitating advanced linear attacks. We apply the algorithm to 17 different ciphers, and present new and improved results on several of these

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

New method for combining Matsui’s bounding conditions with sequential encoding method

As the first generic method for finding the optimal differential and linear characteristics, Matsui\u27s branch and bound search algorithm has played an important role in evaluating the security of symmetric ciphers. By combining Matsui\u27s bounding conditions with automatic search models, search efficiency can be improved. In this paper, by studying the properties of Matsui\u27s bounding conditions, we give the general form of bounding conditions that can eliminate all the impossible solutions determined by Matsui\u27s bounding conditions. Then, a new method of combining bounding conditions with sequential encoding method is proposed. With the help of some small size Mixed Integer Linear Programming (MILP) models, we can use fewer variables and clauses to build Satisfiability Problem (SAT) models. As applications, we use our new method to search for the optimal differential and linear characteristics of some SPN, Feistel, and ARX block ciphers. The number of variables and clauses and the solving time of the SAT models are decreased significantly. In addition, we find some new differential and linear characteristics covering more rounds

Automatic Search of Bit-Based Division Property for ARX Ciphers and Word-Based Division Property

Division property is a generalized integral property proposed by Todo at Eurocrypt 2015. Previous tools for automatic searching are mainly based on the Mixed Integer Linear Programming (MILP) method and trace the division property propagation at the bit level. In this paper, we propose automatic tools to detect ARX ciphers\u27 division property at the bit level and some specific ciphers\u27 division property at the word level. For ARX ciphers, we construct the automatic searching tool relying on Boolean Satisfiability Problem (SAT) instead of MILP, since SAT method is more suitable in the search of ARX ciphers\u27 differential/linear characteristics. The propagation of division property is translated into a system of logical equations in Conjunctive Normal Form (CNF). Some logical equations can be dynamically adjusted according to different initial division properties and stopping rule, while the others corresponding to r-round propagations remain the same. Moreover, our approach can efficiently identify some optimized distinguishers with lower data complexity. As a result, we obtain a 17-round distinguisher for SHACAL-2, which gains four more rounds than previous work, and an 8-round distinguisher for LEA, which covers one more round than the former one. For word-based division property, we develop the automatic search based on Satisfiability Modulo Theories (SMT), which is a generalization of SAT. We model division property propagations of basic operations and S-boxes by logical formulas, and turn the searching problem into an SMT problem. With some available solvers, we achieve some new distinguishers. For CLEFIA, 10-round distinguishers are obtained, which cover one more round than the previous work. For the internal block cipher of Whirlpool, the data complexities of 4/5-round distinguishers are improved. For Rijndael-192 and Rijndael-256, 6-round distinguishers are presented, which attain two more rounds than the published ones. Besides, the integral attacks for CLEFIA are improved by one round with the newly obtained distinguishers

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

New Insights On Differential And Linear Bounds Using Mixed Integer Linear Programming (Full Version)

Mixed Integer Linear Programming (MILP) is a very common method of modelling differential and linear bounds for ciphers, as it automates the process of finding the best differential trail or linear approximation. The Convex Hull (CH) modelling, introduced by Sun et al. (Eprint 2013/Asiacrypt 2014), is a popular method in this regard, which can convert the conditions corresponding to a small (4-bit) SBox to MILP constraints efficiently. In our work, we study this modelling with CH in more depth and observe a previously unreported problem associated with it. Our analysis shows, there are SBoxes for which the CH modelling can yield incorrect modelling. As such, using the CH modelling may lead to incorrect differential or linear bounds. This arises from the observation that although the CH is generated for a certain set of points, there can be points outside this set which also satisfy all the inequalities of the CH. As apparently no variant of the CH modelling can circumvent this problem, we propose a new modelling for differential and linear bounds. Our modelling makes use of every points of interest individually. This modelling works for an arbitrary SBox, and is able to find the exact bound. Additionally, we also explore the possibility of using redundant constraints, such that the run time for an MILP solver can be reduced while keeping the optimal result unchanged. For this purpose, we revisit the CH modelling and use the CH constraints as redundant constraints (on top of our usual constraints, which ensure the aforementioned problem does not occur). In fact, we choose two heuristics from the convex hull modelling. The first uses all the inequalities of a convex hull, while second uses a reduced number of inequalities. Apart from that, we also propose to use the solutions for the smaller rounds as another heuristic to find the optimal bound for a higher round. With our experiments on round-reduced GIFT-128, we show it is possible to reduce the run time a few folds using a suitable choice of redundant constraints. Further, we observe the necessity to consider separate heuristics for the differential and linear cases. We also present the optimal linear bounds for 11- and 12-rounds of GIFT-128, extending from the best-known result of 10-rounds

Machine Learning Assisted Differential Distinguishers For Lightweight Ciphers (Extended Version)

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\u27 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\u27s 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