Success Probability of Multiple/Multidimensional Linear Cryptanalysis Under General Key Randomisation Hypotheses

This work considers statistical analysis of attacks on block ciphers using several linear approximations. A general and unified approach is adopted. To this end, the general key randomisation hypotheses for multidimensional and multiple linear cryptanalysis are introduced. Expressions for the success probability in terms of the data complexity and the advantage are obtained using the general key randomisation hypotheses for both multidimensional and multiple linear cryptanalysis and under the settings where the plaintexts are sampled with or without replacement. Particularising to standard/adjusted key randomisation hypotheses gives rise to success probabilities in 16 different cases out of which in only five cases expressions for success probabilities have been previously reported. Even in these five cases, the expressions for success probabilities that we obtain are more general than what was previously obtained. A crucial step in the analysis is the derivation of the distributions of the underlying test statistics. While we carry out the analysis formally to the extent possible, there are certain inherently heuristic assumptions that need to be made. In contrast to previous works which have implicitly made such assumptions, we carefully highlight these and discuss why they are unavoidable. Finally, we provide a complete characterisation of the dependence of the success probability on the data complexity

Roadmap on optical security

Postprint (author's final draft

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

Algebraic properties of generalized Rijndael-like ciphers

We provide conditions under which the set of Rijndael functions considered as permutations of the state space and based on operations of the finite field \GF (p^k) ($p\geq 2$ a prime number) is not closed under functional composition. These conditions justify using a sequential multiple encryption to strengthen the AES (Rijndael block cipher with specific block sizes) in case AES became practically insecure. In Sparr and Wernsdorf (2008), R. Sparr and R. Wernsdorf provided conditions under which the group generated by the Rijndael-like round functions based on operations of the finite field \GF (2^k) is equal to the alternating group on the state space. In this paper we provide conditions under which the group generated by the Rijndael-like round functions based on operations of the finite field \GF (p^k) ($p\geq 2$) is equal to the symmetric group or the alternating group on the state space.Comment: 22 pages; Prelim0

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

Random Permutation Statistics and An Improved Slide-Determine Attack on KeeLoq

KeeLoq is a lightweight block cipher which is extensively used in the automotive industry. Its periodic structure, and overall simplicity makes it vulnerable to many different attacks. Only certain attacks are considered as really "practical" attacks on KeeLoq: the brute force, and several other attacks which require up to 2p16 known plaintexts and are then much faster than brute force, developed by Courtois et al., and (faster attack) by Dunkelman et al. On the other hand, due to the unusually small block size, there are yet many other attacks on KeeLoq, which require the knowledge of as much as about 2p32 known plaintexts but are much faster still. There are many scenarios in which such attacks are of practical interest, for example if a master key can be recovered, see Section 2 in [11] for a detailed discussion. The fastest of these attacks is an attack by Courtois, Bard and Wagner from that has a very low complexity of about 2p28 KeeLoq encryptions on average. In this paper we will propose an improved and refined attack which is faster both on average and in the best case. We also present an exact mathematical analysis of probabilities that arise in these attacks using the methods of modern analytic combinatorics