Multiple Differential Cryptanalysis using \LLR and χ2\chi^2 Statistics

Recent block ciphers have been designed to be resistant against differential cryptanalysis. Nevertheless it has been shown that such resistance claims may not be as tight as wished due to recent advances in this field. One of the main improvements to differential cryptanalysis is the use of many differentials to reduce the data complexity. In this paper we propose a general model for understanding multiple differential cryptanalysis and propose new attacks based on tools used in multidimensional linear cryptanalysis (namely \LLR and \CHI statistical tests). Practical cases are considered on a reduced version of the cipher PRESENT to evaluate different approaches for selecting and combining the differentials considered. We also consider the tightness of the theoretical estimates corresponding to these attacks

Another Look at Normal Approximations in Cryptanalysis

Statistical analysis of attacks on symmetric ciphers often require assuming the normal behaviour of a test statistic. Typically such an assumption is made in an asymptotic sense. In this work, we consider concrete versions of some important normal approximations that have been made in the literature. To do this, we use the Berry-Esséen theorem to derive explicit bounds on the approximation errors. Analysing these error bounds in the cryptanalytic context throws up several surprising results. One important implication is that this puts in doubt the applicability of the order statistics based approach for analysing key recovery attacks on block ciphers. This approach has been earlier used to obtain several results on the data complexities of (multiple) linear and differential cryptanalysis. The non-applicability of the order statistics based approach puts a question mark on the data complexities obtained using this approach. Fortunately, we are able to recover all of these results by utilising the hypothesis testing framework. Detailed consideration of the error in normal approximation also has implications for $\chi^2$ and the log-likelihood ratio (LLR) based test statistics. The normal approximation of the $\chi^2$ test statistics has some serious and counter-intuitive restrictions. One such restriction is that for multiple linear cryptanalysis as the number of linear approximations grows so does the requirement on the number of plaintext-ciphertext pairs for the approximation to be proper. The issue of satisfactorily addressing the problems with the application of the $\chi^2$ test statistics remains open. For the LLR test statistics, previous work used a normal approximation followed by another approximation to simplify the parameters of the normal approximation. We derive the error bound for the normal approximation which turns out to be difficult to interpret. We show that the approximation required for simplifying the parameters restricts the applicability of the result. Further, we argue that this approximation is actually not required. More generally, the message of our work is that all cryptanalytic attacks should properly derive and interpret the error bounds for any normal approximation that is made

Statistical Tests for Key Recovery Using Multidimensional Extension of Matsui\u27s Algorithm 1

In one dimension, there is essentially just one binomially distributed statistic, bias or correlation, for testing correctness of a key bit in Matsui\u27s Algorithm 1. In multiple dimensions, different statistical approaches for finding the correct key candidate are available. The purpose of this work is to investigate the efficiency of such test in theory and practice, and propose a new key class ranking statistic using distributions based on multidimensional linear approximation and generalisation of the ranking statistic presented by Selc cuk

Multidimensional linear cryptanalysis

Linear cryptanalysis is an important tool for studying the security of symmetric ciphers. In 1993 Matsui proposed two algorithms, called Algorithm 1 and Algorithm 2, for recovering information about the secret key of a block cipher. The algorithms exploit a biased probabilistic relation between the input and output of the cipher. This relation is called the (one-dimensional) linear approximation of the cipher. Mathematically, the problem of key recovery is a binary hypothesis testing problem that can be solved with appropriate statistical tools. The same mathematical tools can be used for realising a distinguishing attack against a stream cipher. The distinguisher outputs whether the given sequence of keystream bits is derived from a cipher or a random source. Sometimes, it is even possible to recover a part of the initial state of the LFSR used in a key stream generator. Several authors considered using many one-dimensional linear approximations simultaneously in a key recovery attack and various solutions have been proposed. In this thesis a unified methodology for using multiple linear approximations in distinguishing and key recovery attacks is presented. This methodology, which we call multidimensional linear cryptanalysis, allows removing unnecessary and restrictive assumptions. We model the key recovery problems mathematically as hypothesis testing problems and show how to use standard statistical tools for solving them. We also show how the data complexity of linear cryptanalysis on stream ciphers and block ciphers can be reduced by using multiple approximations. We use well-known mathematical theory for comparing different statistical methods for solving the key recovery problems. We also test the theory in practice with reduced round Serpent. Based on our results, we give recommendations on how multidimensional linear cryptanalysis should be used

Rigorous Upper Bounds on Data Complexities of Block Cipher Cryptanalysis

Statistical analysis of symmetric key attacks aims to obtain an expression for the data complexity which is the number of plaintext-ciphertext pairs needed to achieve the parameters of the attack. Existing statistical analyses invariably use some kind of approximation, the most common being the approximation of the distribution of a sum of random variables by a normal distribution. Such an approach leads to expressions for data complexities which are {\em inherently approximate}. Prior works do not provide any analysis of the error involved in such approximations. In contrast, this paper takes a rigorous approach to analysing attacks on block ciphers. In particular, no approximations are used. Expressions for upper bounds on the data complexities of several basic and advanced attacks are obtained. The analysis is based on the hypothesis testing framework. Probabilities of Type-I and Type-II errors are upper bounded using standard tail inequalities. In the cases of single linear and differential cryptanalysis, we use the Chernoff bound. For the cases of multiple linear and multiple differential cryptanalysis, Hoeffding bounds are used. This allows bounding the error probabilities and obtaining expressions for data complexities. We believe that our method provides important results for the attacks considered here and more generally, the techniques that we develop should have much wider applicability

A New Test Statistic for Key Recovery Attacks Using Multiple Linear Approximations

The log-likelihood ratio (LLR) and the chi-squared distribution based test statistics have been proposed in the literature for performing statistical analysis of key recovery attacks on block ciphers. A limitation of the LLR test statistic is that its application requires the full knowledge of the corresponding distribution. Previous work using the chi-squared approach required {\em approximating} the distribution of the relevant test statistic by chi-squared and normal distributions. Problematic issues regarding such approximations have been reported in the literature. Perhaps more importantly, both the LLR and the chi-squared based methods are applicable only if the success probability $P_S$ is greater than 0.5. On the other hand, an attack with success probability less than $0.5$ is also of considerable interest. This work proposes a new test statistic for key recovery attacks which has the following features. Its application does not require the full knowledge of the underlying distribution; it is possible to carry out an analysis using this test statistic without using any approximations; the method applies for all values of the success probability. The statistical analysis of the new test statistic follows the hypothesis testing framework and uses Hoeffding\u27s inequalities to bound the probabilities of Type-I and Type-II errors

New Links Between Differential and Linear Cryptanalysis

Recently, a number of relations have been established among previously known statistical attacks on block ciphers. Leander showed in 2011 that statistical saturation distinguishers are on average equivalent to multidimensional linear distinguishers. Further relations between these two types of distinguishers and the integral and zero-correlation distinguishers were established by Bogdanov et al.. Knowledge about such relations is useful for classification of statistical attacks in order to determine those that give essentially complementary information about the security of block ciphers. The purpose of the work presented in this paper is to explore relations between differential and linear attacks. The mathematical link between linear and differential attacks was discovered by Chabaud and Vaudenay already in 1994, but it has never been used in practice. We will show how to use it for computing accurate estimatesof truncated differential probabilities from accurate estimates of correlations of linear approximations. We demonstrate this method in practice and give the first instantiation of multiple differential cryptanalysis using the LLR statistical test on PRESENT. On a more theoretical side,we establish equivalence between a multidimensional linear distinguisher and a truncated differential distinguisher, and show that certain zero-correlation linear distinguishers exist if and only if certain impossible differentials exist

Further Improving Differential-Linear Attacks: Applications to Chaskey and Serpent

Differential-linear attacks are a cryptanalysis family that has recently benefited from various technical improvements, mainly in the context of ARX constructions. In this paper we push further this refinement, proposing several new improvements. In particular, we develop a better understanding of the related correlations, improve upon the statistics by using the LLR, and finally use ideas from conditional differentials for finding many right pairs. We illustrate the usefulness of these ideas by presenting the first 7.5-round attack on Chaskey. Finally, we present a new competitive attack on 12 rounds of Serpent, and as such the first cryptanalytic progress on Serpent in 10 years

Multiple Differential Cryptanalysis: A Rigorous Analysis

Statistical analyses of multiple differential attacks are considered in this paper. Following the work of Blondeau and Gérard, the most general situation of multiple differential attack where there are no restrictions on the set of differentials is studied. We obtain closed form bounds on the data complexity in terms of the success probability and the advantage of an attack. This is done under two scenarios -- one, where an independence assumption used by Blondeau and Gérard is assumed to hold and second, where no such assumption is made. The first case employs the Chernoff bounds while the second case uses the Hoeffding bounds from the theory of concentration inequalities. In both cases, we do not make use of any approximations in our analysis. As a consequence, the results are more generally applicable compared to previous works. The analysis without the independence assumption is the first of its kind in the literature. We believe that the current work places the statistical analysis of multiple differential attack on a more rigorous foundation than what was previously known

Nonlinear cryptanalysis of reduced-round Serpent and metaheuristic search for S-box approximations.

We utilise a simulated annealing algorithm to find several nonlinear approximations to various S-boxes which can be used to replace the linear approximations in the outer rounds of existing attacks. We propose three variants of a new nonlinear cryptanalytic algorithm which overcomes the main issues that prevented the use of nonlinear approximations in previous research, and we present the statistical frameworks for calculating the complexity of each version. We present new attacks on 11-round Serpent with better data complexity than any other known-plaintext or chosen-plaintext attack, and with the best overall time complexity for a 256-bit key