Machine Learning Assisted Differential Distinguishers For Lightweight Ciphers (Extended Version)

At CRYPTO 2019, Gohr first introduces the deep learning based cryptanalysis on round-reduced SPECK. Using a deep residual network, Gohr trains several neural network based distinguishers on 8-round SPECK-32/64. The analysis follows an `all-in-one\u27 differential cryptanalysis approach, which considers all the output differences effect under the same input difference. Usually, the all-in-one differential cryptanalysis is more effective compared to the one using only one single differential trail. However, when the cipher is non-Markov or its block size is large, it is usually very hard to fully compute. Inspired by Gohr\u27s work, we try to simulate the all-in-one differentials for non-Markov ciphers through machine learning. Our idea here is to reduce a distinguishing problem to a classification problem, so that it can be efficiently managed by machine learning. As a proof of concept, we show several distinguishers for four high profile ciphers, each of which works with trivial complexity. In particular, we show differential distinguishers for 8-round Gimli-Hash, Gimli-Cipher and Gimli-Permutation; 3-round Ascon-Permutation; 10-round Knot-256 permutation and 12-round Knot-512 permutation; and 4-round Chaskey-Permutation. Finally, we explore more on choosing an efficient machine learning model and observe that only a three layer neural network can be used. Our analysis shows the attacker is able to reduce the complexity of finding distinguishers by using machine learning techniques

New Insights On Differential And Linear Bounds Using Mixed Integer Linear Programming (Full Version)

Mixed Integer Linear Programming (MILP) is a very common method of modelling differential and linear bounds for ciphers, as it automates the process of finding the best differential trail or linear approximation. The Convex Hull (CH) modelling, introduced by Sun et al. (Eprint 2013/Asiacrypt 2014), is a popular method in this regard, which can convert the conditions corresponding to a small (4-bit) SBox to MILP constraints efficiently. In our work, we study this modelling with CH in more depth and observe a previously unreported problem associated with it. Our analysis shows, there are SBoxes for which the CH modelling can yield incorrect modelling. As such, using the CH modelling may lead to incorrect differential or linear bounds. This arises from the observation that although the CH is generated for a certain set of points, there can be points outside this set which also satisfy all the inequalities of the CH. As apparently no variant of the CH modelling can circumvent this problem, we propose a new modelling for differential and linear bounds. Our modelling makes use of every points of interest individually. This modelling works for an arbitrary SBox, and is able to find the exact bound. Additionally, we also explore the possibility of using redundant constraints, such that the run time for an MILP solver can be reduced while keeping the optimal result unchanged. For this purpose, we revisit the CH modelling and use the CH constraints as redundant constraints (on top of our usual constraints, which ensure the aforementioned problem does not occur). In fact, we choose two heuristics from the convex hull modelling. The first uses all the inequalities of a convex hull, while second uses a reduced number of inequalities. Apart from that, we also propose to use the solutions for the smaller rounds as another heuristic to find the optimal bound for a higher round. With our experiments on round-reduced GIFT-128, we show it is possible to reduce the run time a few folds using a suitable choice of redundant constraints. Further, we observe the necessity to consider separate heuristics for the differential and linear cases. We also present the optimal linear bounds for 11- and 12-rounds of GIFT-128, extending from the best-known result of 10-rounds

Finding Three-Subset Division Property for Ciphers with Complex Linear Layers (Full Version)

Conventional bit-based division property (CBDP) and bit- based division property using three subsets (BDPT) introduced by Todo et al. at FSE 2016 are the most effective techniques for finding integral characteristics of symmetric ciphers. At ASIACRYPT 2019, Wang et al. proposed the idea of modeling the propagation of BDPT, and recently Liu et al. described a model set method that characterized the BDPT propagation. However, the linear layers of the block ciphers which are analyzed using the above two methods of BDPT propagation are restricted to simple bit permutation. Thus the feasibility of the MILP method of BDPT propagation to analyze ciphers with complex linear layers is not settled. In this paper, we focus on constructing an automatic search algorithm that can accurately characterize BDPT propagation for ciphers with complex linear layers. We first introduce BDPT propagation rule for the binary diffusion layer and model that propagation in MILP efficiently. The solutions to these inequalities are exact BDPT trails of the binary diffusion layer. Next, we propose a new algorithm that models Key-Xor operation in BDPT based on MILP technique. Based on these ideas, we construct an automatic search algorithm that accurately characterizes the BDPT propagation and we prove the correctness of our search algorithm. We demonstrate our model for the block ciphers with non-binary diffusion layers by decomposing the non-binary linear layer trivially by the COPY and XOR operations. Therefore, we apply our method to search integral distinguishers based on BDPT of SIMON, SIMON(102), PRINCE, MANTIS, PRIDE, and KLEIN block ciphers. For PRINCE and MANTIS, we find (2 + 2) and (3 + 3) round integral distinguishers respectively which are longest to date. We also improve the previous best integral distinguishers of PRIDE and KLEIN. For SIMON, SIMON(102), the integral distinguishers found by our method are consistent with the existing longest distinguishers

Zero-Correlation Attacks on Tweakable Block Ciphers with Linear Tweakey Expansion

The design and analysis of dedicated tweakable block ciphers is a quite recent and very active research field that provides an ongoing stream of new insights. For instance, results of Kranz, Leander, and Wiemer from FSE 2017 show that the addition of a tweak using a linear tweak schedule does not introduce new linear characteristics. In this paper, we consider – to the best of our knowledge – for the first time the effect of the tweak on zero-correlation linear cryptanalysis for ciphers that have a linear tweak schedule. It turns out that the tweak can often be used to get zero-correlation linear hulls covering more rounds compared to just searching zero-correlation linear hulls on the data-path of a cipher. Moreover, this also implies the existence of integral distinguishers on the same number of rounds. We have applied our technique on round reduced versions of Qarma, Mantis, and Skinny. As a result, we can present – to the best of our knowledge – the best attack (with respect to number of rounds) on a round-reduced variant of Qarma

Finding Integral Distinguishers with Ease

The division property method is a technique to determine integral distinguishers on block ciphers. While the complexity of finding these distinguishers is higher, it has recently been shown that MILP and SAT solvers can efficiently find such distinguishers. In this paper, we provide a framework to automatically find those distinguishers which solely requires a description of the cryptographic primitive. We demonstrate that by finding integral distinguishers for 30 primitives with different design strategies. We provide several new or improved bit-based division property distinguishers for ChaCha, Chaskey, DES, GIFT, LBlock, Mantis, Qarma, RoadRunner, Salsa and SM4. Furthermore, we present an algorithm to find distinguishers with lower data complexity more efficiently