Finding Bit-Based Division Property for Ciphers with Complex Linear Layers

The bit-based division property (BDP) is the most effective technique for finding integral characteristics of symmetric ciphers. Recently, automatic search tools have become one of the most popular approaches to evaluating the security of designs against many attacks. Constraint-aided automatic tools for the BDP have been applied to many ciphers with simple linear layers like bit-permutation. Constructing models of complex linear layers accurately and efficiently remains hard. A straightforward method proposed by Sun et al. (called the S method), decomposes a complex linear layer into basic operations like COPY and XOR, then models them one by one. However, this method can easily insert invalid division trails into the solution pool, which results in a quicker loss of the balanced property than the cipher itself would. In order to solve this problem, Zhang and Rijmen propose the ZR method to link every valid trail with an invertible sub-matrix of the matrix corresponding to the linear layer, and then generate linear inequalities to represent all the invertible sub-matrices. Unfortunately, the ZR method is only applicable to invertible binary matrices (defined in Definition 3).To avoid generating a huge number of inequalities for all the sub-matrices, we build a new model that only includes that the sub-matrix corresponding to a valid trail should be invertible. The computing scale of our model can be tackled by most of SMT/SAT solvers, which makes our method practical. For applications, we improve the previous BDP for LED and MISTY1. We also give the 7-round BDP results for Camellia with FL/FL−1, which is the longest to date.Furthermore, we remove the restriction of the ZR method that the matrix has to be invertible, which provides more choices for future designs. Thanks to this, we also reproduce 5-round key-dependent integral distinguishers proposed at Crypto 2016 which cannot be obtained by either the S or ZR methods

When Intrusion Detection Meets Blockchain Technology: A Review

With the purpose of identifying cyber threats and possible incidents, intrusion detection systems (IDSs) are widely deployed in various computer networks. In order to enhance the detection capability of a single IDS, collaborative intrusion detection networks (or collaborative IDSs) have been developed, which allow IDS nodes to exchange data with each other. However, data and trust management still remain two challenges for current detection architectures, which may degrade the effectiveness of such detection systems. In recent years, blockchain technology has shown its adaptability in many fields, such as supply chain management, international payment, interbanking, and so on. As blockchain can protect the integrity of data storage and ensure process transparency, it has a potential to be applied to intrusion detection domain. Motivated by this, this paper provides a review regarding the intersection of IDSs and blockchains. In particular, we introduce the background of intrusion detection and blockchain, discuss the applicability of blockchain to intrusion detection, and identify open challenges in this direction

Design of Lightweight Linear Diffusion Layers from Near-MDS Matrices

Near-MDS matrices provide better trade-offs between security and efficiency compared to constructions based on MDS matrices, which are favored for hardwareoriented designs. We present new designs of lightweight linear diffusion layers by constructing lightweight near-MDS matrices. Firstly generic n×n near-MDS circulant matrices are found for 5 ≤ n ≤9. Secondly, the implementation cost of instantiations of the generic near-MDS matrices is examined. Surprisingly, for n = 7, 8, it turns out that some proposed near-MDS circulant matrices of order n have the lowest XOR count among all near-MDS matrices of the same order. Further, for n = 5, 6, we present near-MDS matrices of order n having the lowest XOR count as well. The proposed matrices, together with previous construction of order less than five, lead to solutions of n×n near-MDS matrices with the lowest XOR count over finite fields F2m for 2 ≤ n ≤ 8 and 4 ≤ m ≤ 2048. Moreover, we present some involutory near-MDS matrices of order 8 constructed from Hadamard matrices. Lastly, the security of the proposed linear layers is studied by calculating lower bounds on the number of active S-boxes. It is shown that our linear layers with a well-chosen nonlinear layer can provide sufficient security against differential and linear cryptanalysis

On the division property of S-boxes

In 2015, Todo introduced a property of multisets of a finite field called the division property. It is then used by Todo in an attack against the S7 S-box of the MISTY1 cipher. This paper provides a complete mathematical analysis of the division property. The tool we use is the discrete Fourier transform. We relate the division property to the natural concept of the degree of a subset of a finite field. This indeed provides a characterization of multisets satisfying the division property. In 2015, Sun et al. gave some properties related to the division property. In this paper we give a complete characterization and reprove many of their results. We show that the division property is actually the dual of the degree of $t$-products of the inverse S-box and show these two characteristics are affine invariants. We then propose a very efficient way to check vulnerability of a given S-box against attacks of this type. We also reprove some recent interesting results using the method based on the discrete Fourier transform. We finally check whether the S-boxes of the candidate ciphers in the CAESAR competition are vulnerable against attacks based on the division property

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

Zero-Sum Partitions of PHOTON Permutations

We describe an approach to zero-sum partitions using Todo’s division property at EUROCRYPT 2015. It follows the inside-out methodology, and includes MILP-assisted search for the forward and backward trails, and subspace approach to connect those two trails that is less restrictive than commonly done. As an application we choose PHOTON, a family of sponge-like hash function proposals that was recently standardized by ISO. With respect to the security claims made by the designers, we for the first time show zero-sum partitions for almost all of those full 12-round permutation variants that use a 4-bit S-Box. As with essentially any other zero-sum property in the literature, also here the gap between a generic attack and the shortcut is small

Combining MILP Modeling with Algebraic Bias Evaluation for Linear Mask Search: Improved Fast Correlation Attacks on SNOW

The Mixed Integer Linear Programming (MILP) technique has been widely applied in the realm of symmetric-key cryptanalysis. In this paper, we propose a new bitwise breakdown MILP modeling strategy for describing the linear propagation rules of modular addition-based operations. We apply such new techniques to cryptanalysis of the SNOW stream cipher family and find new linear masks: we use the MILP model to find many linear mask candidates among which the best ones are identified with particular algebraic bias evaluation techniques. For SNOW 3G, the correlation of the linear mask we found is the highest on record: such results are highly likely to be optimal according to our analysis. For SNOW 2.0, we find new masks matching the correlation record and many new sub-optimal masks applicable to improving correlation attacks. For SNOW-V/Vi, by investigating both bitwise and truncated linear masks, we find all linear masks having the highest correlation, and prove the optimum of the corresponding truncated patterns under the ``fewest active S-box preferred\u27\u27 strategy. By using the newly found linear masks, we give correlation attacks on the SNOW family with improved complexities. We emphasize that the newly proposed uniform MILP-aided framework can be potentially applied to analyze LFSR-FSM structures composed of modular addition and S-box as non-linear components

Approximate Modeling of Signed Difference and Digraph based Bit Condition Deduction: New Boomerang Attacks on BLAKE

The signed difference is a powerful tool for analyzing the Addition, XOR, Rotation (ARX) cryptographic primitives. Currently, solving the accurate model for the signed difference propagation is infeasible. We propose an approximate MILP modeling method capturing the propagation rules of signed differences. Unlike the accurate signed difference model, the approximate model only focuses on active bits and ignores the possible bit conditions on inactive bits. To overcome the negative effect of a lower accuracy arising from ignoring bit conditions on inactive bits, we propose an additional tool for deducing all bit conditions automatically. Such a tool is based on a directed-graph capturing the whole computation process of ARX primitives by drawing links among intermediate words and operations. The digraph is also applicable in the MILP model construction process: it enables us to identify the parameters upper bounding the number of bit conditions so as to define the objective function; it is further used to connect the boomerang top and bottom signed differential paths by introducing proper constraints to avoid incompatible intersections. Benefiting from the approximate model and the directed-graph based tool, the solving time of the new MILP model is significantly reduced, enabling us to deduce signed differential paths efficiently and accurately. To show the utility of our method, we propose boomerang attacks on the keyed permutations of three ARX hash functions of BLAKE. For the first time we mount an attack on the full 7 rounds of BLAKE3, with the complexity as low as $2^{180}$. Our best attack on BLAKE2s can improve the previously best result by 0.5 rounds but with lower complexity. The attacks on BLAKE-256 cover the same 8 rounds with the previous best result but with complexity $2^{16}$ times lower. All our results are verified practically with round-reduced boomerang quartets

Person Tracking Using Audio and Depth Cues

In this paper, a novel probabilistic Bayesian tracking scheme is proposed and applied to bimodal measurements consisting of tracking results from the depth sensor and audio recordings collected using binaural microphones. We use random finite sets to cope with varying number of tracking targets. A measurement-driven birth process is integrated to quickly localize any emerging person. A new bimodal fusion method that prioritizes the most confident modality is employed. The approach was tested on real room recordings and experimental results show that the proposed combination of audio and depth outperforms individual modalities, particularly when there are multiple people talking simultaneously and when occlusions are frequent