Error Function Attack of chaos synchronization based encryption schemes

Different chaos synchronization based encryption schemes are reviewed and compared from the practical point of view. As an efficient cryptanalysis tool for chaos encryption, a proposal based on the Error Function Attack is presented systematically and used to evaluate system security. We define a quantitative measure (Quality Factor) of the effective applicability of a chaos encryption scheme, which takes into account the security, the encryption speed, and the robustness against channel noise. A comparison is made of several encryption schemes and it is found that a scheme based on one-way coupled chaotic map lattices performs outstandingly well, as judged from Quality Factor

Using quantum key distribution for cryptographic purposes: a survey

The appealing feature of quantum key distribution (QKD), from a cryptographic viewpoint, is the ability to prove the information-theoretic security (ITS) of the established keys. As a key establishment primitive, QKD however does not provide a standalone security service in its own: the secret keys established by QKD are in general then used by a subsequent cryptographic applications for which the requirements, the context of use and the security properties can vary. It is therefore important, in the perspective of integrating QKD in security infrastructures, to analyze how QKD can be combined with other cryptographic primitives. The purpose of this survey article, which is mostly centered on European research results, is to contribute to such an analysis. We first review and compare the properties of the existing key establishment techniques, QKD being one of them. We then study more specifically two generic scenarios related to the practical use of QKD in cryptographic infrastructures: 1) using QKD as a key renewal technique for a symmetric cipher over a point-to-point link; 2) using QKD in a network containing many users with the objective of offering any-to-any key establishment service. We discuss the constraints as well as the potential interest of using QKD in these contexts. We finally give an overview of challenges relative to the development of QKD technology that also constitute potential avenues for cryptographic research.Comment: Revised version of the SECOQC White Paper. Published in the special issue on QKD of TCS, Theoretical Computer Science (2014), pp. 62-8

Analysis of NORX: Investigating Differential and Rotational Properties

This paper presents a thorough analysis of the AEAD scheme NORX, focussing on differential and rotational properties. We first introduce mathematical models that describe differential propagation with respect to the non-linear operation of NORX. Afterwards, we adapt a framework previously proposed for ARX designs allowing us to automatise the search for differentials and characteristics. We give upper bounds on the differential probability for a small number of steps of the NORX core permutation. For example, in a scenario where an attacker can only modify the nonce during initialisation, we show that characteristics have probabilities of less than $2^{-60}$ ($32$-bit) and $2^{-53}$ ($64$-bit) after only one round. Furthermore, we describe how we found the best characteristics for four rounds, which have probabilities of $2^{-584}$ ($32$-bit) and $2^{-836}$ ($64$-bit), respectively. Finally, we discuss some rotational properties of the core permutation which yield some first, rough bounds and can be used as a basis for future studies

Cryptographic strength of a new symmetric block cipher based on Feistel network

The paper summarizes research on the cryptographic strength of a new symmetric block cipher based on the Feistel network. The classification of cryptographic attacks, depending on the cryptanalyst’s input data, is considered. For the purpose of testing, the linear and differential cryptanalysis as well as the Slide attack were used

Methods for Linear and Differential Cryptanalysis of Elastic Block Ciphers

The elastic block cipher design employs the round function of a given, b-bit block cipher in a black box fashion, embedding it in a network structure to construct a family of ciphers in a uniform manner. The family is parameterized by block size, for any size between b and 2b. The design assures that the overall workload for encryption is proportional to the block size. When considering the approach taken in elastic block ciphers, the question arises as to whether cryptanalysis results, including methods of analysis and bounds on security, for the original fixed-sized cipher are lost or, since original components of the cipher are used, whether previous analysis can be applied or reused in some manner. With this question in mind, we analyze elastic block ciphers and consider the security against two basic types of attacks, linear and differential cryptanalysis. We show how they can be related to the corresponding security of the fixed-length version of the cipher. Concretely, we develop techniques that take advantage of relationships between the structure of the elastic network and the original version of the cipher, independently of the cipher. This approach demonstrates how one can build upon existing components to allow cryptanalysis within an extended structure (a topic which may be of general interest outside of elastic block ciphers). We show that any linear attack on an elastic block cipher can be converted efficiently into a linear attack on the fixed-length version of the cipher by converting the equations used to attack the elastic version to equations for the fixed-length version. We extend the result to any algebraic attack. We then define a general method for deriving the differential characteristic bound of an elastic block cipher using the differential bound on a single round of the fixed-length version of the cipher. The structure of elastic block ciphers allows us to use a state transition method to compute differentials for the elastic version from differentials of the round function of the original cipher

CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using side-channel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.Comment: 15 pages, 16 figure

Links between Division Property and Other Cube Attack Variants

A theoretically reliable key-recovery attack should evaluate not only the non-randomness for the correct key guess but also the randomness for the wrong ones as well. The former has always been the main focus but the absence of the latter can also cause self-contradicted results. In fact, the theoretic discussion of wrong key guesses is overlooked in quite some existing key-recovery attacks, especially the previous cube attack variants based on pure experiments. In this paper, we draw links between the division property and several variants of the cube attack. In addition to the zero-sum property, we further prove that the bias phenomenon, the non-randomness widely utilized in dynamic cube attacks and cube testers, can also be reflected by the division property. Based on such links, we are able to provide several results: Firstly, we give a dynamic cube key-recovery attack on full Grain-128. Compared with Dinur et al.’s original one, this attack is supported by a theoretical analysis of the bias based on a more elaborate assumption. Our attack can recover 3 key bits with a complexity 297.86 and evaluated success probability 99.83%. Thus, the overall complexity for recovering full 128 key bits is 2125. Secondly, now that the bias phenomenon can be efficiently and elaborately evaluated, we further derive new secure bounds for Grain-like primitives (namely Grain-128, Grain-128a, Grain-V1, Plantlet) against both the zero-sum and bias cube testers. Our secure bounds indicate that 256 initialization rounds are not able to guarantee Grain-128 to resist bias-based cube testers. This is an efficient tool for newly designed stream ciphers for determining the number of initialization rounds. Thirdly, we improve Wang et al.’s relaxed term enumeration technique proposed in CRYPTO 2018 and extend their results on Kreyvium and ACORN by 1 and 13 rounds (reaching 892 and 763 rounds) with complexities 2121.19 and 2125.54 respectively. To our knowledge, our results are the current best key-recovery attacks on these two primitives

An Assessment of Differential-Neural Distinguishers

Since the introduction of differential-neural cryptanalysis, as the machine learning assisted differential cryptanalysis proposed in [Goh19] is coined by now, a lot of followup works have been published, showing the applicability for a wide variety of ciphers. In this work, we set out to vet a multitude of differential-neural distinguishers presented so far, and additionally provide general insights. Firstly, we show for a selection of different ciphers how differential-neural distinguishers for those ciphers can be (automatically) optimized, also providing guidance to do so for other ciphers as well. Secondly, we explore a correlation between a differential-neural distinguisher\u27s accuracy and a standard notion of difference between the two underlying distributions. Furthermore, we show that for a whole (practically relevant) class of ciphers, the differential-neural distinguisher can use differential features only. At last, we also rectify a common mistake in current literature, and show that, making use of an idea already presented in the foundational work[Goh19], the claimed improvements from using multiple ciphertext-pairs at once are at most marginal, if not non-existent

Slide Attacks on a Class of Hash Functions

Abstract. This paper studies the application of slide attacks to hash functions. Slide attacks have mostly been used for block cipher cryptanalysis. But, as shown in the current paper, they also form a potential threat for hash functions, namely for sponge-function like structures. As it turns out, certain constructions for hash-function-based MACs can be vulnerable to forgery and even to key recovery attacks. In other cases, we can at least distinguish a given hash function from a random oracle. To illustrate our results, we describe attacks against the Grindahl-256 and Grindahl-512 hash functions. To the best of our knowledge, this is the first cryptanalytic result on Grindahl-512. Furthermore, we point out a slide-based distinguisher attack on a slightly modified version of RadioGatún. We finally discuss simple countermeasures as a defense against slide attacks. Key words: slide attacks, hash function, Grindahl, RadioGatún, MAC, sponge function.