Security Challenges in Smart-Grid Metering and Control Systems

The smart grid is a next-generation power system that is increasingly attracting the attention of government, industry, and academia. It is an upgraded electricity network that depends on two-way digital communications between supplier and consumer that in turn give support to intelligent metering and monitoring systems. Considering that energy utilities play an increasingly important role in our daily life, smart-grid technology introduces new security challenges that must be addressed. Deploying a smart grid without adequate security might result in serious consequences such as grid instability, utility fraud, and loss of user information and energy-consumption data. Due to the heterogeneous communication architecture of smart grids, it is quite a challenge to design sophisticated and robust security mechanisms that can be easily deployed to protect communications among different layers of the smart grid-infrastructure. In this article, we focus on the communication-security aspect of a smart-grid metering and control system from the perspective of cryptographic techniques, and we discuss different mechanisms to enhance cybersecurity of the emerging smart grid. We aim to provide a comprehensive vulnerability analysis as well as novel insights on the cybersecurity of a smart grid

Comparing Large-unit and Bitwise Linear Approximations of SNOW 2.0 and SNOW 3G and Related Attacks

In this paper, we study and compare the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G, and present a fast correlation attack on SNOW 3G by using our newly found bitwise linear approximations. On one side, we reconsider the relation between the large-unit linear approximation and the smallerunit/ bitwise ones derived from the large-unit one, showing that approximations on large-unit alphabets have advantages over all the smaller-unit/bitwise ones in linear attacks. But then on the other side, by comparing the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G respectively, we have found many concrete examples of 8-bit linear approximations whose certain 1-dimensional/bitwise linear approximations have almost the same SEI (Squared Euclidean Imbalance) as that of the original 8-bit ones. That is, each of these byte-wise linear approximations is dominated by a single bitwise approximation, and thus the whole SEI is not essentially larger than the SEI of the dominating single bitwise approximation. Since correlation attacks can be more efficiently implemented using bitwise approximations rather than large-unit approximations, improvements over the large-unit linear approximation attacks are possible for SNOW 2.0 and SNOW 3G. For SNOW 3G, we make a careful search of the bitwise masks for the linear approximations of the FSM and obtain many mask tuples which yield high correlations. By using these bitwise linear approximations, we mount a fast correlation attack to recover the initial state of the LFSR with the time/memory/data/pre-computation complexities all upper bounded by 2174.16, improving slightly the previous best one which used an 8-bit (vectorized) linear approximation in a correlation attack with all the complexities upper bounded by 2176.56. Though not a significant improvement, our research results illustrate that we have an opportunity to achieve improvement over the large-unit attacks by using bitwise linear approximations in a linear approximation attack, and provide a new insight on the relation between large-unit and bitwise linear approximations

New Algorithms for Solving LPN

The intractability of solving the LPN problem serves as the security source of many lightweight/post-quantum cryptographic schemes proposed over the past decade. There are several algorithms available so far to fulfill the solving task. In this paper, we present further algorithmic improvements to the existing work. We describe the first efficient algorithm for the single-list $k$-sum problem which naturally arises from the various BKW reduction settings, propose the hybrid mode of BKW reduction and show how to compute the matrix multiplication in the Gaussian elimination step with flexible and reduced time/memory complexities. The new algorithms yield the best known tradeoffs on the %time/memory/data complexity curve and clearly compromise the $80$-bit security of the LPN instances suggested in cryptographic schemes. Practical experiments on reduced LPN instances verified our results

Fast Correlation Attacks on Grain-like Small State Stream Ciphers

In this paper, we study the security of Grain-like small state stream ciphers by fast correlation attacks, which are commonly regarded as classical cryptanalytic methods against LFSR-based stream ciphers. We extend the cascaded structure adopted in such primitives in general and show how to restore the full internal state part-by-part if the non-linear combining function meets some characteristic. As a case study, we present a key recovery attack against Fruit, a tweaked version of Sprout that employs key-dependent state updating in the keystream generation phase. Our attack requires 262.8 Fruit encryptions and 222.3 keystream bits to determine the 80-bit secret key. Practical simulations on a small-scale version confirmed our results

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

An Ultra-Efficient Key Recovery Attack on the Lightweight Stream Cipher A2U2

In this letter we report on an ultra-efficient key recovery attack under the chosen-plaintext-attack model against the stream cipher A2U2, which is the most lightweight cryptographic primitive (i.e., it costs only 284 GE in hardware implementation) proposed so far for low-cost Radio Frequency Identification (RFID) tags. Our attack can fully recover the secret key of the A2U2 cipher by only querying the A2U2 encryption twice on the victim tag and solving 32 sparse systems of linear equations (where each system has 56 unknowns and around 28 unknowns can be directly obtained without computation) in the worst case, which takes around 0.16 second on a Thinkpad T410 laptop

TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration

We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities. Unlike existing works that generate dance movements using a single modality such as music, our goal is to produce richer dance movements guided by the instructive information provided by the text. However, the lack of paired motion data with both music and text modalities limits the ability to generate dance movements that integrate both. To alleviate this challenge, we propose to utilize a 3D human motion VQ-VAE to project the motions of the two datasets into a latent space consisting of quantized vectors, which effectively mix the motion tokens from the two datasets with different distributions for training. Additionally, we propose a cross-modal transformer to integrate text instructions into motion generation architecture for generating 3D dance movements without degrading the performance of music-conditioned dance generation. To better evaluate the quality of the generated motion, we introduce two novel metrics, namely Motion Prediction Distance (MPD) and Freezing Score, to measure the coherence and freezing percentage of the generated motion. Extensive experiments show that our approach can generate realistic and coherent dance movements conditioned on both text and music while maintaining comparable performance with the two single modalities. Code will be available at: https://garfield-kh.github.io/TM2D/