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Optimal Linear Codes With Few Weights From Simplicial Complexes

IEEE Transactions on Information Theory 2025年第5期71卷 3531-3543页

作者： Chen, Bing Xu, Yunge Hu, Zhao Li, Nian Zeng, Xiangyong Hubei University Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Wuhan430062 China Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Wuhan430062 China State Key Laboratory of Integrated Service Networks Xi’an710071 China Hubei University Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Key Laboratory of Intelligent Sensing System and Security Ministry of Education Wuhan430062 China

Recently, constructions of optimal linear codes from simplicial complexes have attracted much attention and some related nice works were presented. Let q be a prime power. In this paper, by using the simplicial complexes of Fmq with one single maximal element, we construct four families of linear codes over the ring Fq+uFq}( u2=0 ), which generalizes the results of Wu et al. (2020). The parameters and Lee weight distributions of these four families of codes are completely determined. Most notably, via the Gray map, we obtain several classes of optimal linear codes over Fq, including (near) Griesmer codes and distance-optimal codes. Moreover, it is shown that most of the Gray images are minimal or self-orthogonal codes which are useful in applications. © 1963-2012 IEEE.

关键词： Turbo codes

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Enabling intelligent Connectivity: A Survey of Secure ISAC in 6G networks

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 748-781页

作者： Zhu, Xiaoqiang Liu, Jiqiang Lu, Lingyun Zhang, Tao Qiu, Tie Wang, Chunpeng Liu, Yuan Beijing Jiaotong University School of Software Engineering Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University College of Intelligence and Computing Tianjin300350 China Qilu University of Technology School of Cyber Security and the Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250353 China

The rapid growth of intelligent sensing capabilities and super computation power in 6G mobile communication systems has facilitated their application in diverse domains such as smart health, smart factories, and the industrial Internet of Things. Integrated Sensing and Communication (ISAC), as a core technology, has merged with artificial intelligence (AI) to enable intelligent connectivity, leading to a paradigm shift in traditional communication modes. This paper presents a visionary design for an ISAC-oriented unified IoT architecture that integrates software-defined communication and super-intelligent agents. By leveraging dynamic adaptability, self-learning, and optimization, the ISAC system can intelligently and flexibly respond to evolving requirements and environments. The architecture is redefined into three layers: the hardware layer, the omniscient layer, and the application layer. Furthermore, a retrospective survey of ISAC technology development over the past decade is conducted, highlighting new design principles for AI-empowered networks and multi-modals that support "intelligent connectivity" across various application scenarios and reinforce the security of ISAC. This paper categorizes the related works according to the different layer structures of the proposed architecture, and some important physical and machine learning models are introduced. Additionally, we summarize the current technological bottlenecks associated with ISAC and propose future research directions and potential solutions that lay the foundation for the future development of secure and intelligent communication networks. © 1998-2012 IEEE.

关键词： Computer architecture

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Lip-Audio Modality Fusion for Deep Forgery Video Detection

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Computers, Materials & Continua 2025年第2期82卷 3499-3515页

作者： Yong Liu Zhiyu Wang Shouling Ji Daofu Gong Lanxin Cheng Ruosi Cheng College of Cyberspace Security Information Engineering UniversityZhengzhou450001China Research Institute of Intelligent Networks Zhejiang LabHangzhou311121China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China Key Laboratory of Cyberspace Security Ministry of EducationZhengzhou450001China

In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.

关键词： Deep forgery video detection lip-audio modality fusion mel frequency cepstrum coefficient siamese neural network band-pass filter

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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Secure Multi-way Join Query and Its Bench-Marking with Trusted Execution Environments 2nd

Secure Multi-way Join Query and Its Bench-Marking with Tru...

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2nd International Conference on Data security and Privacy Protection, DSPP 2024

作者： Zhao, Yi Zhao, Sen Lv, Siyi School of Cyber Engineering Xidian University Xi’an China State Key Laboratory of Integrated Service Networks Xidian University Xi’an China Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin China

ISBN: (纸本)9789819785452

Because of server-untrustable model in outsourcing encrypted database system, it’s fatal for data owners to keep their data confidential under complex query operations performed by database servers. Trusted Execution Environments (TEEs) provide a secure space for privacy computation via hardware-based methods to increase the efficiency of database system, and it ensures the availability of computing directly on sensitive data in untrusted outsourcing database servers. However, while various query schemes are designed under the combination of encrypted database and TEEs, designing an available and secure scheme for join queries on multiple data tables with TEEs remains an open problem. Besides, previous works also devote less to bench-marking multi-way joins on TEEs. In our work, we extend binary join query scheme into multi-way forms in encrypted database system based on TEEs. It combines various join algorithms based on hash functions (hash-based, radix-based, etc.) with parallelized optimizing mechanism. Besides, we do bench-marking with TEEBench framework on those multi-way join schemes in three-way forms using both cache-fit and cache-exceed datasets and do different evaluation on two TEEs hardware platforms (Intel SGX and AMD SEV) to show its availability. After that, we also propose a multi-way join scheme with access pattern protection through the combination between our previous scheme and oblivious I/O methods. With the evaluation of its efficiency and memory leakage, we can also verify the feasibility of our access-pattern-protected multi-way join scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Outsourcing

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Proactive Robust Hardening of Resilient Power Distribution network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 14953-14966页

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong Xi’an Jiaotong University School of Automation Science and Engineering Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi’an710049 China University of Pittsburgh Department of Industrial Engineering Department of Electrical and Computer Engineering PittsburghPA15106 United States Tsinghua University Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Xi’an Jiaotong University School of Electrical Engineering Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitude. © 2004-2012 IEEE.

关键词： Stochastic programming

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Multiple Edge Data Integrity Verification with Multi-Vendors and Multi-Servers in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4668-4683页

作者： Islam, Md Rashedul Xiang, Yong Uddin, Md Palash Zhao, Yao Kua, Jonathan Gao, Longxiang The School of Information Technology Deakin University GeelongVIC3220 Australia The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Ensuring Edge Data Integrity (EDI) is imperative in providing reliable and low-latency services in mobile edge computing. Existing EDI schemes typically address single-vendor (App Vendor, AV) single-server (Edge Server, ES), single-vendor multi-server, and multi-vendor multi-server scenarios, which consider a single data replica cached by an ES from the AVs. However, the most practical scenario of Multi-Vendors and Multi-Servers with Multiple Data (MVMS-MD) cached by an ES from different AVs remains unexplored. Current solutions struggle when applied to this scenario due to increased computation and communication costs in the verification process across all ESs using the classical challenge-response per-data multi-round strategy. To tackle this issue, we propose a Multiple EDI-Verification (MEDI-V) approach in this paper. In particular, our MEDI-V utilizes an adaptive Merkle Hash Tree (ad-MHT) to efficiently generate a tree of multiple data replicas within each AV. Next, the dynamic mechanism computes minimal verification information using ad-MHT to create a challenge for individual ESs to produce EDI proofs. The ES then leverages its ad-MHT and the ES’s proof to send the reconstructed ad-MHT root to the AV for verification. Theoretical insights into MEDI-V’s correctness, efficiency, security, and comprehensive evaluations demonstrate its superiority in addressing MEDI issues in the MVMS-MD scenario. © 2002-2012 IEEE.

关键词： Data integrity

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N3PA-STIN: A Novel Three-Party Authentication Protocol for Multi-User Access in Satellite-Terrestrial Integrated networks

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IEEE Internet of Things Journal 2025年第10期12卷 14952-14968页

作者： Guo, Junyan Yao, Shuang Song, Yue Han, Xu Zheng, Zhi Chang, Liyuan China Telecom Cybersecurity Technology Co. Ltd Beijing China China Telecom Co. Ltd Operation Center of Network Security Products Beijing China China Electric Power Research Institute Co. Ltd Beijing China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing China

Satellite-Terrestrial Integrated network (STIN) serves as essential infrastructure for providing seamless global coverage and wireless remote subscription services. However, the inherent heterogeneity, satellite exposure, and the openness of satellite-terrestrial links pose significant security challenges for authentication, such as privacy breaches, eavesdropping, replay attacks, and identity impersonation, as well as scalability issues like dynamic node joining and revocation. Existing authentication protocols suffer from deficiencies in unlinkability, scalability, and resistance to multiple attacks, and often rely on overly optimistic assumptions regarding satellite trustworthiness. Moreover, performance bottlenecks in handling numerous user access authentication requests within short timeframes remain unresolved. To address these challenges, we propose the N3PA-STIN protocol, a novel three-party authentication protocol for multi-user access that ensures mutual trust among users, satellites, and ground stations. The protocol minimizes computational overhead through an efficient batch verification mechanism, and enhances privacy and unlinkability by employing temporary identifiers derived from one-time pseudonyms. Furthermore, the protocol ensures conditional anonymity, enabling accountability while preserving user privacy, and achieves conditional verifiability by restricting the verification of authentication messages exclusively to registered nodes. A domain key update mechanism based on the Chinese Remainder Theorem (CRT) supports dynamic node management, effectively addressing the scalability challenges in heterogeneous networks. security and performance analyses demonstrate that the N3PA-STIN protocol meets the security requirements and minimizes both computational and communication overhead, making it a practical and effective solution for STIN. © 2014 IEEE.

关键词： Authentication Protocol

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EvolGCN: A Co-evolutionary Graph Convolutional network Model for Dynamically Spatio-Temporal Anomaly Event Inference

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Liu, Xiaoming Pu, Hang Chen, Zhuo Zhang, Zhanwei Lan, Yu Shen, Chao Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

Accurately spatio-temporal anomaly event inference is significant to enhance society's safety, such as crime prevention and traffic collision reduction, etc. However, it is hard to achieve good performance for its complicated process being influenced by various kinds of factors. Previous works mainly focus on employing feature-based regression or fitting models with supposed spatio-temporal distribution to tackle this challenge, but are normally short of the following considerations: 1) mutual evolutionary influence, i.e. The dynamic evolution of interactions and dependencies among anomaly events, is changing along with the timeline and alters the probabilities or patterns of their occurrences dynamically;2) messy features, i.e. complex attributes in the data but with noise, are difficult to select and aggregate for the representation learning algorithm with uncertainty and redundancy. To address the research gap, we put forward a co-evolutionary graph convolutional network model to explicate the dynamically spatio-temporal anomaly patterns. Specifically, we firstly take advantage of a fuzzy-rough set-based algorithm to select features by discovering the specialty and permanence attributes of different interaction features. Then, we propose a co-evolutionary learning method to embed the dynamically temporal influence into latent features with selected interaction information. Finally, we design a graph convolutional network with an attention mechanism to formulate the mutually spatial effects among the anomaly events. The proposed model is verified on the New York City crime records from the real-world, and extensive experiments show that our approach achieves 0.10129, 0.09958 and 0.10034 of the MAE (hour) in the action, location, and action-location time inference tasks, and 0.7973 and 0.4678 of the accuracy in the action and location type inference tasks, which outperform state-of-arts by 24.00%, 50.60%, 11.10%, 10.56% and 21.53% at most, respectively. Hyper-param

关键词： Convolutional neural networks

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 IEEE.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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