Cryptanalysis of the MALICIOUS Framework

This note describes several attacks on the MALICIOUS framework for creating backdoored tweakable block ciphers. It is shown that, although the embedded malicious tweak pair itself is hard to recover, it is feasible to find additional weak tweak pairs that can be used to mount key-recovery attacks. Full-round attacks on most instances of LowMC-M are given. Our attacks are far from optimized and significant future improvements are to be expected. We focus on low-data attacks, since these are the most relevant for typical use-cases of LowMC. In addition, this implies that our attacks can not be prevented by limiting the amount of data that can be encrypted using the weak tweak pair. Despite our findings, we believe that the MALICIOUS framework can be used to create backdoored variants of LowMC provided that the parameters are modified

Improved Interpolation Attacks on Cryptographic Primitives of Low Algebraic Degree

Symmetric cryptographic primitives with low multiplicative complexity have been proposed to improve the performance of emerging applications such as secure Multi-Party Computation. However, primitives composed of round functions with low algebraic degree require a careful evaluation to assess their security against algebraic cryptanalysis, and in particular interpolation attacks. This paper proposes new low-memory interpolation attacks on symmetric key primitives of low degree. Moreover, we present generic attacks on block ciphers with a simple key schedule; our attacks require either constant memory or constant data complexity. The improved attack is applied to the block cipher MiMC which aims to minimize the number of multiplications in large finite fields. As a result, we can break MiMC-$129/129$ with $38$ rounds with time and data complexity $2^{65.5}$ and $2^{60.2}$ respectively and with negligible memory; this attack invalidates one of the security claims of the designers. Our attack indicates that for MiMC-$129/129$ the full $82$ rounds are necessary even with restrictions on the memory available to the attacker. For variants of MiMC with larger keys, we present new attacks with reduced complexity. Our results do not affect the security claims of the full round MiMC

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

Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs

Recognizing driver behaviors is becoming vital for in-vehicle systems that seek to reduce the incidence of car accidents rooted in cognitive distraction. In this paper, we harness the exceptional feature extraction abilities of deep learning and propose a dedicated Interwoven Deep Convolutional Neural Network (InterCNN) architecture to tackle the accurate classification of driver behaviors in real-time. The proposed solution exploits information from multi-stream inputs, i.e., in-vehicle cameras with different fields of view and optical flows computed based on recorded images, and merges through multiple fusion layers abstract features that it extracts. This builds a tight ensembling system, which significantly improves the robustness of the model. We further introduce a temporal voting scheme based on historical inference instances, in order to enhance accuracy. Experiments conducted with a real world dataset that we collect in a mock-up car environment demonstrate that the proposed InterCNN with MobileNet convolutional blocks can classify 9 different behaviors with 73.97% accuracy, and 5 aggregated behaviors with 81.66% accuracy. Our architecture is highly computationally efficient, as it performs inferences within 15ms, which satisfies the real-time constraints of intelligent cars. In addition, our InterCNN is robust to lossy input, as the classification remains accurate when two input streams are occluded

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

Correlation of Quadratic Boolean Functions: Cryptanalysis of All Versions of Full MORUS

We show that the correlation of any quadratic Boolean function can be read out from its so-called disjoint quadratic form. We further propose a polynomial-time algorithm that can transform an arbitrary quadratic Boolean function into its disjoint quadratic form. With this algorithm, the exact correlation of quadratic Boolean functions can be computed efficiently. We apply this method to analyze the linear trails of MORUS (one of the seven finalists of the CAESAR competition), which are found with the help of a generic model for linear trails of MORUS-like key-stream generators. In our model, any tool for finding linear trails of block ciphers can be used to search for trails of MORUS-like key-stream generators. As a result, a set of trails with correlation $2^{-38}$ is identified for all versions of full MORUS, while the correlations of previously published best trails for MORUS-640 and MORUS-1280 are $2^{-73}$ and $2^{-76}$ respectively (ASIACRYPT 2018). This significantly improves the complexity of the attack on MORUS-1280-256 from $2^{152}$ to $2^{76}$. These new trails also lead to the first distinguishing and message-recovery attacks on MORUS-640-128 and MORUS-1280-128 with surprisingly low complexities around $2^{76}$. Moreover, we observe that the condition for exploiting these trails in an attack can be more relaxed than previously thought, which shows that the new trails are superior to previously published ones in terms of both correlation and the number of ciphertext blocks involved

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.gi

Learning Driven Mobility Control of Airborne Base Stations in Emergency Networks

International audienceMobile base stations mounted on unmanned aerial vehicles (UAVs) provide viable wireless coverage solutions in challenging landscapes and conditions, where cellular/WiFi infrastructure is unavailable. Operating multiple such airborne base stations, to ensure reliable user connectivity, demands intelligent control of UAV movements, as poor signal strength and user outage can be catastrophic to mission critical scenarios. In this paper, we propose a deep reinforcement learning based solution to tackle the challenges of base stations mobility control. We design an Asynchronous Advantage Actor-Critic (A3C) algorithm that employs a custom reward function, which incorporates SINR and outage events information, and seeks to provide mobile user coverage with the highest possible signal quality. Preliminary results reveal that our solution converges after 4 × 10 5 steps of training, after which it outperforms a benchmark gradient-based alternative, as we attain 5dB higher median SINR during an entire test mission of 10,000 steps

Learning Driven Mobility Control of Airborne Base Stations in Emergency Networks

International audienceMobile base stations mounted on unmanned aerial vehicles (UAVs) provide viable wireless coverage solutions in challenging landscapes and conditions, where cellular/WiFi infrastructure is unavailable. Operating multiple such airborne base stations, to ensure reliable user connectivity, demands intelligent control of UAV movements, as poor signal strength and user outage can be catastrophic to mission critical scenarios. In this paper, we propose a deep reinforcement learning based solution to tackle the challenges of base stations mobility control. We design an Asynchronous Advantage Actor-Critic (A3C) algorithm that employs a custom reward function, which incorporates SINR and outage events information, and seeks to provide mobile user coverage with the highest possible signal quality. Preliminary results reveal that our solution converges after 4 × 10 5 steps of training, after which it outperforms a benchmark gradient-based alternative, as we attain 5dB higher median SINR during an entire test mission of 10,000 steps

Observations on the Dynamic Cube Attack of 855-Round TRIVIUM from Crypto\u2718

Recently, another kind of dynamic cube attack is proposed by Fu et al. With some key guesses and a transformation in the output bit, they claim that, when the key guesses are correct, the degree of the transformed output bit can drop so significantly that the cubes of lower dimension can not exist, making the output bit vulnerable to the zero-sum cube tester using slightly higher dimensional cubes. They applied their method to 855-round TRIVIUM. In order to verify the correctness of their result, they even proposed a practical attack on 721-round TRIVIUM claiming that the transformed output bit after 721-rounds of initialization does not contain cubes of dimensions 31 and below. However, the degree evaluation algorithm used by Fu et al. is innovative and complicated, and its complexity is not given. Their algorithm can only be implemented on huge clusters and cannot be verified by existing theoretic tools. In this paper, we theoretically analyze the dynamic cube attack method given by Fu et al. using the division property and MILP modeling technique. Firstly, we draw links between the division property and Fu et al.\u27s dynamic cube attack so that their method can be described as a theoretically well founded and computationally economic MILP-aided division-property-based cube attack. With the MILP model drawn according to the division property, we analyzed the 721-round TRIVIUM in detail and find some interesting results: \begin​{enumerate} \item The degree evaluation using our MILP method is more accurate than that of Fu et al.\u27s. Fu et al. prove that the degree of pure z721z721 is 40 while our method gives 29. We practically proved the correctness of our method by trying thousands of random keys, random 30-dimensional cubes and random assignments to non-cube IVs finding that the summations are constantly 0. \item For the transformed output bit (1+s2901)⋅z721(1+s1290)⋅z721, we proved the same degree 31 as Fu et al. and we also find 32-dimensional cubes have zero-sum property for correct key guesses. But since the degree of pure z721z721 is only 29, the 721-round practical attack on TRIVIUM is violating the principle of Fu et al.\u27s work: after the transformation in the output bit, when the key guesses are correct, the degree of the transformed output bit has not dropped but risen. \item Now that the degree theoretic foundation of the 721-round attack has been violated, we also find out that the key-recovery attack cannot be carried out either. We theoretically proved and practically verified that no matter the key guesses are correct or incorrect, the summation over 32-dimensional cube are always 0. So, no key bit can be recovered at all. \end{enumerate} All these analysis on 721-round TRIVIUM can be verified practically and we open our C++ source code for implementation as well. Secondly, we revisit their 855-round result. Our MILP model reveal that the 855-round result suffers from the same problems with its 721-round counterpart. We provide theoretic evidence that, after their transformation, the degree of the output bit is more likely to rise rather than drop. Furthermore, since Fu \etal\u27s degree evaluation is written in an unclear manner and no complexity analysis is given, we rewrite the algorithm according to their main ideas and supplement a detailed complexity analysis. Our analysis indicates that a precise evaluation to the degree requires complexities far beyond practical reach. We also demonstrate that further abbreviation to our rewritten algorithm can result in wrong evaluation. This might be the reason why Fu \etal give such a degree evaluation. This is also an additional argument against Fu \etal\u27s dynamic cube attack method. Thirdly, the selection of Fu \etal\u27s cube dimension is also questionable. According to our experiments and existing theoretic results, there is high risk that the correct key guesses and wrong ones share the same zero-sum property using Fu \etal\u27s cube testers. As a remedy, we suggest that concrete cubes satisfying particular conditions should be identified rather than relying on the IV-degree drop hypothesis. To conclude, Fu \etal\u27s dynamic cube attack on 855-round TRIVIUM is questionable. 855-round as well as 840-and-up-round TRIVIUM should still be open for further convincible cryptanalysis