China Electronics Corporation (China)

Movable antennas (MAs) are a promising technology to achieve a significant enhancement in rate for future wireless networks. The pioneering investigation on near-field directional modulation design for a reconfigurable intelligent surface (RIS)-assisted MA system is presented, with the base station equipped with a MA array. To maximize the secrecy sum rate (Max-SSR) with hardware impairments (HWIs) and imperfect channel state information (CSI), which involves a joint optimization of beamforming vectors for confidential messages and artificial noise (AN), power allocation factors, phase shift matrices, MA positions, and receive beamforming vectors. Firstly, the transmit beamforming vectors and phase shift matrices are iteratively optimized, leveraging leakage theory and phase alignment techniques. Then, two novel algorithms for discrete MA positioning are proposed, respectively, employing uniform and compressed sensing (CS)-based non-uniform grouping strategies. Subsequently, the AN is considered and designed as the additional energy required for zero-space projection, and the receive beamforming vector is derived using the minimum mean square error (MMSE) method. The proposed algorithms have low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms. Under HWIs and imperfect CSI, the proposed algorithm can achieve a 28% enhancement in SSR performance while reducing the number of antennas by 37.5% compared to traditional fixed-position antenna (FPA) systems.

In this paper, we present CTRU-Light, an IND-CCA-secure key encapsulation mechanism (KEM) derived from NTRU and RLWE (and RLWR in variant) assumptions over power-of-two cyclotomic rings. Our CTRU-Light employs a compact NTT-compatible modulus q=641 while maintaining minimal public key and ciphertext dimensions with negligible error probability. Specifically, the design yields public key and ciphertext sizes of 1206 bytes under an error probability bound of ≤2−110. When benchmarked against Kyber (NIST’s sole standardized KEM), CTRU-Light demonstrates 23.0–30.0% lower bandwidth consumption, accelerates key generation by at least 6.0%, and achieves over 1.3× speed enhancement in both encapsulation and decapsulation procedures.

This study examines the anomalous Hall effect (AHE) in the Heusler series ${\mathrm{Mn}}_{3}Z$ ($Z$=Ga, Ge, Sn), with a particular emphasis on the manipulation of noncollinear antiferromagnetic structures to enhance the AHE. By employing density-functional theory and first-principles calculations, we demonstrate that the anomalous Hall conductivity is markedly responsive to electron filling. By strategically doping Ga into ${\mathrm{Mn}}_{3}\mathrm{Sn}$ and ${\mathrm{Mn}}_{3}\mathrm{Ge}$ in order to modulate the electron density, a significant increase in anomalous Hall conductivity (AHC) is achieved. It is noteworthy that a Ga:Sn ratio of 1:5 yields peak AHC values exceeding $700\phantom{\rule{0.16em}{0ex}}{(\mathrm{\ensuremath{\Omega}}\phantom{\rule{0.16em}{0ex}}\mathrm{cm})}^{\ensuremath{-}1}$, while 3:7 Ga-Ge ratios can result in AHC values surpassing $600\phantom{\rule{0.16em}{0ex}}{(\mathrm{\ensuremath{\Omega}}\phantom{\rule{0.16em}{0ex}}\mathrm{cm})}^{\ensuremath{-}1}$. A comparison between the virtual crystal approximation and supercell construction methods for doping has revealed consistent trends. The results of this study pave the way for optimizing the AHE in noncollinear antiferromagnetic materials.

With the continuous advancement of industrial intelligence, the closed environment of industrial control network is becoming more and more open, and the intranet equipment of industrial control system is facing more risks. In recent years, more attention has been paid to the safety of industrial control network. In order to improve the safety detection efficiency and accuracy of industrial control network and enhance the ability of industrial control network to resist risks, the identification of industrial control equipment types in the network is the foundation. In order to solve the problem that some industrial control equipment can't identify the type of equipment by active detection because they don't give feedback to the received detection packets after setting relevant rules or strategies, this paper constructs a knowledge map of the connectivity between different types of equipment, and then infers the types of other equipment with the connectivity with identifiable types of equipment on the basis of identifying some equipment types in the network.

Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical component of next-generation air traffic management systems. However, its vulnerability to spoofing attacks stems from the broadcast of plaintext messages lacking encryption and authentication, potentially leading to severe consequences. To address this, this paper proposes an LSTM-Transformer Fusion Variational Autoencoder (LTF-VAE) for ADS-B anomaly detection. The key innovations are: (1) The encoder employs a Bidirectional LSTM for local temporal modeling combined with a 3-layer 8-head Transformer for global spatio-temporal modeling. (2) Variational inference is introduced to generate latent space distributions, constraining the model to probabilistically represent normal flight patterns. (3) The decoder utilizes a cascade structure of a single-layer LSTM and a 2-layer Transformer, reconstructing multidimensional flight parameters synchronously via fully connected layers. Experiments demonstrate that the model effectively detects diverse ADS-B anomalies under various attack scenarios, outperforming baseline methods.

With the exceptional ability of deep learning to extract high-dimensional structures from massive datasets, its application in the industrial Internet of Things (IIoT) has become increasingly prevalent. However, the inherent security vulnerabilities of deep learning pose a significant threat to IIoT systems, particularly in the form of backdoor attacks. Current defense methods are primarily designed for image processing tasks, and due to the uniqueness of industrial environments, their effectiveness is significantly reduced because of the lack of precision when applied directly to the IIoT applications. To address these challenges, this article proposes a trigger detection method tailored for industrial environments, capable of precisely calculating the values of triggers during the detection process. Building on this, we introduce a saliency map-based trigger pruning method to further refine the triggers. Finally, utilizing these refined triggers, we perform trigger recovery to complete the backdoor defense against the IIoT model. Furthermore, by integrating these approaches, we construct a comprehensive detection-pruning-recovery defense framework against backdoor attacks in industrial settings. Experimental results across multiple industrial scenarios demonstrate that our method enhances the robustness of industrial applications against backdoor attacks, outperforming existing defense mechanisms.

Reverse engineering of agnostic industrial control protocols (ICPs) based on traffic traces is significant for the security analysis of industrial control systems. Field semantics deduction is an essential step in protocol reverse engineering following the discovery of the message field. Most existing methods rely on knowledge-based analysis for specific fields of common protocols, which require too numerous assumptions and lack semantic knowledge about ICPs. In this paper, we propose a new concept, pattern series, and design the first classification framework for inferring the semantic types of unknown ICPs. Specifically, we first present the definition of pattern series and design the field pattern series generation algorithm for building training data, then develop a field semantics classification model to learn and apply semantic features from known protocols to predict semantic types in unknown protocols. Lastly, we implement a probability-maximizing selection algorithm to obtain optimal semantic types. We demonstrate the effectiveness of the proposed method through extensive experiments with five popular ICPs, including their mixed protocols. Evaluations show that our approach significantly outperforms baseline methods in field semantic recognition, achieving ≥90.8% F1-score.