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

SNR-Centric Power Trace Extractors for Side-Channel Attacks

The existing power trace extractors consider the case that the number of power traces owned by the attacker is sufficient to guarantee his successful attacks, and the goal of power trace extraction is to lower the complexity rather than increase the success rates. Although having strict theoretical proofs, they are too simple and leakage characteristics of POIs have not been thoroughly analyzed. They only maximize the variance of data-dependent power consumption component and ignore the noise component, which results in very limited SNR to improve and seriously affects the performance of extractors. In this paper, we provide a rigorous theoretical analysis of SNR of power traces, and propose a novel SNR-centric extractor, named Shortest Distance First (SDF), to extract power traces with smallest the estimated noise by taking advantage of known plaintexts. In addition, to maximize the variance of the exploitable component while minimizing the noise, we refer to the SNR estimation model and propose another novel extractor named Maximizing Estimated SNR First (MESF). Finally, we further propose an advanced extractor called Mean optimized MESF (MMESF) that exploits the mean power consumption of each plaintext byte value to more accurately and reasonably estimate the data-dependent power consumption of the corresponding samples. Experiments on both simulated power traces and measurements from an ATmega328p micro-controller demonstrate the superiority of our new extractors

Lower data attacks on Advanced Encryption Standard

The Advanced Encryption Standard (AES) is one of the most commonly used and analyzed encryption algorithms. In this work, we present new combinations of some prominent attacks on AES, achieving new records in data requirements among attacks, utilizing only $2^4$ and $2^{16}$ chosen plaintexts (CP) for 6-round and 7-round AES-192/256 respectively. One of our attacks is a combination of a meet-in-the-middle (MiTM) attack with a square attack mounted on 6-round AES-192/256 while another attack combines an MiTM attack and an integral attack, utilizing key space partitioning technique, on 7-round AES-192/256. Moreover, we illustrate that impossible differential (ID) attacks can be viewed as the dual of MiTM attacks in certain aspects which enables us to recover the correct key using the meet-in-the-middle (MiTM) technique instead of sieving through all potential wrong keys in our ID attack. Furthermore, we introduce the constant guessing technique in the inner rounds which significantly reduces the number of key bytes to be searched. The time and memory complexities of our attacks remain marginal

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(log_2(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

A framework for analyzing RFID distance bounding protocols

Many distance bounding protocols appropriate for the RFID technology have been proposed recently. Unfortunately, they are commonly designed without any formal approach, which leads to inaccurate analyzes and unfair comparisons. Motivated by this need, we introduce a unied framework that aims to improve analysis and design of distance bounding protocols. Our framework includes a thorough terminology about the frauds, adversary, and prover, thus disambiguating many misleading terms. It also explores the adversary's capabilities and strategies, and addresses the impact of the prover's ability to tamper with his device. It thus introduces some new concepts in the distance bounding domain as the black-box and white-box models, and the relation between the frauds with respect to these models. The relevancy and impact of the framework is nally demonstrated on a study case: Munilla-Peinado distance bounding protocol

Multivariate Profiling of Hulls for Linear Cryptanalysis

Extensions of linear cryptanalysis making use of multiple approximations, such as multiple and multidimensional linear cryptanalysis, are an important tool in symmetric-key cryptanalysis, among others being responsible for the best known attacks on ciphers such as Serpent and present. At CRYPTO 2015, Huang et al. provided a refined analysis of the key-dependent capacity leading to a refined key equivalence hypothesis, however at the cost of additional assumptions. Their analysis was extended by Blondeau and Nyberg to also cover an updated wrong key randomization hypothesis, using similar assumptions. However, a recent result by Nyberg shows the equivalence of linear dependence and statistical dependence of linear approximations, which essentially invalidates a crucial assumption on which all these multidimensional models are based. In this paper, we develop a model for linear cryptanalysis using multiple linearly independent approximations which takes key-dependence into account and complies with Nyberg’s result. Our model considers an arbitrary multivariate joint distribution of the correlations, and in particular avoids any assumptions regarding normality. The analysis of this distribution is then tailored to concrete ciphers in a practically feasible way by combining a signal/noise decomposition approach for the linear hulls with a profiling of the actual multivariate distribution of the signal correlations for a large number of keys, thereby entirely avoiding assumptions regarding the shape of this distribution. As an application of our model, we provide an attack on 26 rounds of present which is faster and requires less data than previous attacks, while using more realistic assumptions and far fewer approximations. We successfully extend the attack to present the first 27-round attack which takes key-dependence into account

Improved Meet-in-the-Middle Attacks on Reduced-Round Kalyna-128/256 and Kalyna-256/512

Kalyna is an SPN-based block cipher that was selected during Ukrainian National Public Cryptographic Competition (2007-2010) and its slight modification was approved as the new encryption standard of Ukraine. In this paper, we focus on the key-recovery attacks on reduced-round Kalyna-128/256 and Kalyna-256/512 with meet-in-the-middle method. The differential enumeration technique and key-dependent sieve technique which are popular to analyze AES are used to attack them. Using the key-dependent sieve technique to improve the complexity is not an easy task, we should build some tables to achieve this. Since the encryption procedure of Kalyna employs a pre- and post-whitening operations using addition modulo $2^{64}$ applied on the state columns independently, we carefully study the propagation of this operation and propose an addition plaintext structure to solve this. For Kalyna-128/256, we propose a 6-round distinguisher, and achieve a 9-round (out of total 14-round) attack. For Kalyna-256/512, we propose a 7-round distinguisher, then achieve an 11-round (out of total 18-round) attack. As far as we know, these are currently the best results on Kalyna-128/256 and Kalyna-256/512

Improving Key-Recovery in Linear Attacks: Application to 28-Round PRESENT

International audienceLinear cryptanalysis is one of the most important tools in usefor the security evaluation of symmetric primitives. Many improvementsand refinements have been published since its introduction, and manyapplications on different ciphers have been found. Among these upgrades,Collard et al. proposed in 2007 an acceleration of the key-recovery partof Algorithm 2 for last-round attacks based on the FFT.In this paper we present a generalized, matrix-based version of the pre-vious algorithm which easily allows us to take into consideration an ar-bitrary number of key-recovery rounds. We also provide efficient variantsthat exploit the key-schedule relations and that can be combined withmultiple linear attacks.Using our algorithms we provide some new cryptanalysis on PRESENT,including, to the best of our knowledge, the first attack on 28 rounds