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 results on Gimli: full-permutation distinguishers and improved collisions

International audienceGimli is a family of cryptographic primitives (both a hash function and an AEAD scheme) that has been selected for the second round of the NIST competition for standardizing new lightweight designs. The candidate Gimli is based on the permutation Gimli, which was presented at CHES 2017. In this paper, we study the security of both the permutation and the constructions that are based on it. We exploit the slow diffusion in Gimli and its internal symmetries to build, for the first time, a distinguisher on the full permutation of complexity 2 64. We also provide a practical distinguisher on 23 out of the full 24 rounds of Gimli that has been implemented. Next, we give (full state) collision and semi-free-start collision attacks on Gimli-Hash, reaching respectively up to 12 and 18 rounds. On the practical side, we compute a collision on 8-round Gimli-Hash. In the quantum setting, these attacks reach 2 more rounds. Finally, we perform the first study of linear trails in the permutation, and we propose differential-linear cryptanalysis that reach up to 17 rounds of Gimli

Triathlon of Lightweight Block Ciphers for the Internet of Things

In this paper, we introduce a framework for the benchmarking of lightweight block ciphers on a multitude of embedded platforms. Our framework is able to evaluate the execution time, RAM footprint, as well as binary code size, and allows one to define a custom "figure of merit" according to which all evaluated candidates can be ranked. We used the framework to benchmark implementations of 19 lightweight ciphers, namely AES, Chaskey, Fantomas, HIGHT, LBlock, LEA, LED, Piccolo, PRESENT, PRIDE, PRINCE, RC5, RECTANGLE, RoadRunneR, Robin, Simon, SPARX, Speck, and TWINE, on three microcontroller platforms: 8-bit AVR, 16-bit MSP430, and 32-bit ARM. Our results bring some new insights into the question of how well these lightweight ciphers are suited to secure the Internet of things. The benchmarking framework provides cipher designers with an easy-to-use tool to compare new algorithms with the state of the art and allows standardization organizations to conduct a fair and consistent evaluation of a large number of candidates

Triathlon of Lightweight Block Ciphers for the Internet of Things

In this paper we introduce a framework for the benchmarking of lightweight block ciphers on a multitude of embedded platforms. Our framework is able to evaluate the execution time, RAM footprint, as well as binary code size, and allows one to define a custom figure of merit according to which all evaluated candidates can be ranked. We used the framework to benchmark implementations of 19 lightweight ciphers, namely AES, Chaskey, Fantomas, HIGHT, LBlock, LEA, LED, Piccolo, PRESENT, PRIDE, PRINCE, RC5, RECTANGLE, RoadRunneR, Robin, Simon, SPARX, Speck, and TWINE, on three microcontroller platforms: 8-bit AVR, 16-bit MSP430, and 32-bit ARM. Our results bring some new insights to the question of how well these lightweight ciphers are suited to secure the Internet of Things (IoT). The benchmarking framework provides cipher designers with an easy-to-use tool to compare new algorithms with the state-of-the-art and allows standardization organizations to conduct a fair and consistent evaluation of a large number of candidates

Improved Differential-Linear Cryptanalysis of 7-Round Chaskey with Partitioning

International audienceIn this work we study the security of Chaskey, a recent lightweight MAC designed by Mouha et al., currently being considered for standardization by ISO/IEC and ITU-T. Chaskey uses an ARX structure very similar to SipHash. We present the first cryptanalysis of Chaskey in the single user setting, with a differential-linear attack against 6 and 7 rounds, hinting that the full version of Chaskey with 8 rounds has a rather small security margin. In response to these attacks, a 12-round version has been proposed by the designers. To improve the complexity of the differential-linear cryptanalysis, we refine a partitioning technique recently proposed by Biham and Carmeli to improve the linear cryptanalysis of addition operations. We also propose an analogue improvement of differential cryptanalysis of addition operations. Roughly speaking, these techniques reduce the data complexity of linear and differential attacks, at the cost of more processing time per data. It can be seen as the analogue for ARX ciphers of partial key guess and partial decryption for SBox-based ciphers. When applied to the differential-linear attack against Chaskey, this partitioning technique greatly reduces the data complexity, and this also results in a reduced time complexity. While a basic differential-linear attack on 7 round takes 2^78 data and time (respectively 2^35 for 6 rounds), the improved attack requires only 2^48 data and 2^67 time (respectively 2^25 data and 2^29 time for 6 rounds). We also show an application of the partitioning technique to FEAL-8X, and we hope that this technique will lead to a better understanding of the security of ARX designs