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Joint communication and computation design for secure integrated sensing and semantic communication system

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Science China(information Sciences) 2025年

作者： Jianxin DAI Hui FAN Zhouxiang ZHAO Yongjun XU Zhaohui YANG Xu GAN Zhaoyang ZHANG School of Science Nanjing University of Posts and Telecommunications National Mobile Communications Research laboratory Southeast University College of information Science and Electronic engineering Zhejiang University Zhejiang Provincial laboratory of Multi-Modal Communication Networks and Intelligent information Processing School of Communications and information engineering Chongqing University of Posts and Telecommunications

As a new communication paradigm, semantic communication (SC) differs from conventional communication based on bit transmission by transmitting semantic information, which demonstrates enormous potential in improving communication efficiency. To ensure transmission security, this paper investigates the secure resource allocation problem of integrated sensing and semantic communication (ISSC) systems in the scenario involving multiple eavesdroppers considering cases of both perfect and imperfect channel state information (CSI). Specifically, the concept of secure semantic rate (SSR) is first put forward to measure the reliability of user information acquisition, and then an optimization problem is formulated to maximize the secure semantic efficiency (SSE), which is defined as the ratio of the sum of SSRs to total transmit power. To work out this problem, the optimization problem is first decoupled into two subproblems, i.e., beamforming optimization subproblem and semantic parameter optimization subproblem. For the beamforming optimization subproblem, this paper simplifies the objective function by utilizing the Dinkelbach algorithm with given semantic parameters and proposes an alternating algorithm to solve a series of convex subproblems iteratively. For the semantic parameter subproblem, the monotonically decreasing property of the objective function with respect to the semantic parameter is revealed mathematically under a given beamforming ***, simulation results verify the theoretical analysis.

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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Computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and information security Guilin University of Electronic TechnologyGuilin541004China School of Electronic engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi engineering Research Center of Industrial internet security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

Correlations multiplexing for link prediction in multidimensional network spaces

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Science China(information Sciences) 2018年第11期61卷 48-66页

作者： Yunpeng XIAO Xixi LI Yuanni LIU Hong LIU Qian LI chongqing engineering laboratory of internet and information security Chongqing University of Posts and Telecommunications

In social networks, link establishment among users is affected by diversity correlations. In this paper, we study the formation of links, map correlations into multidimensional network spaces and apply their behavioral and structural features to the problem of link prediction. First, by exploring user behavioral correlation and network structural correlation, we map them into three network spaces: following space, interaction space and structure space. With a hierarchical process, the coupling relationship between the spaces can be reduced and we can analyze the correlation in each space separately. Second, by taking advantage of the latent Dirichlet allocation(LDA) topic model for dealing with the polysemy and synonym problems, the traditional text modeling method is improved by Gaussian weighting and applied to user behavior *** this way, the expression ability of topics can be enhanced, and improved topic distribution of user behavior can be obtained to mine user correlations in the following space and the interaction space. Moreover, the method can be extended using the hidden naive Bayesian algorithm which is good at reducing attribute independence. By quantifying the dependencies between common neighbors, we can analyze user correlations in the structure space and multiplex the correlations of the other two spaces to link prediction. The experimental results indicate that the method can effectively improve link prediction performance.

关键词： social networks multidimensional network spaces link prediction LDA hidden naive Bayes

Prediction of gas concentration in the upper corner of mining working face based on the FEDformer-LGBM-AT architecture

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Meitan Xuebao/Journal of the China Coal Society 2025年第1期50卷 360-378页

作者： Liang, Yunpei Li, Shang Li, Quangui Guo, Yabo Sun, Wanjie Zheng, Menghao Wang, Chengcheng National Key laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing400044 China College of Resources and security Chongqing University Chongqing400044 China School of Safety Science and engineering Anhui University of Science and Technology Huainan232001 China

In the context of the intelligent upgrading of coal mines, mining high-quality information from massive monitoring data of working faces to construct scientific models that enhance prediction duration and accuracy is crucial for preventing excessive gas concentration in the upper corner. However, there are many factors that affect the gas concentration in the upper corner, and there is a lack of utilization of massive data. The prediction accuracy of gas concentration is high but the duration is short, only 0～30 minutes, while the prediction accuracy and generalization ability are poor for medium to long duration 30～ 60 minutes. In order to solve this problem, this article takes a coal mining face in Shanxi Province as the research object. Firstly, the coal seam gas content of the face is dynamically extracted, and a feature set of coal seam gas content, gas concentration, coal mining machine, and wind speed is constructed. Then, the feature set is preprocessed, and different features are screened based on correlation analysis. Further construct short-term trends, stable trends, periodic trends, and concatenated features of relevant features. Firstly, a gas concentration prediction layer based on frequency enhanced decomposition transformer (FEDformer) is constructed, and a residual correction layer based on lightweight gradient boosting machine (LGBM) is constructed. Then, adaptive thresholding (AT) technology is introduced to construct a threshold perception layer. Finally, a three-layer gas concentration prediction model architecture is formed to predict the gas concentration in the upper corner within the next 60 minutes, and the prediction performance was investigated by recalling rate, false positive rate, MAE and MAPE. The research results indicate that the short-term recall rate of the upper corner gas concentration prediction model based on the FEDformer-LGBM-AT architecture is 0.956, the false alarm rate is 0.035, the MAE is 0.033, and the MAPE is 0.183;Th

关键词： Coal mines

Who will retweet? A prediction method for social hotspots based on dynamic tensor decomposition

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Science China(information Sciences) 2018年第9期61卷 297-299页

作者： Yunpeng XIAO Xiaojuan LI Shasha YANG Yanbing LIU chongqing engineering laboratory of internet and information security Chongqing University of Posts and Telecommunications

With the development of Web 2.0,the emergence of blogs,forums makes the internet to maintain substantial amounts of user behavioral *** data are rich and valuable,and mining useful information from massive data has be... 详细信息

关键词： A prediction method for social hotspots based on dynamic tensor decomposition Who will retweet

SDSA: A Framework of a Software-Defi ned security Architecture

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China Communications 2016年第2期13卷 178-188页

作者： LIU Yanbing LU Xingyu JIAN Yi XIAO Yunpeng chongqing engineering laboratory of Network and information security Chongqing University of Posts and Telecommunications

The fact that the security facilities within a system are closely coupled and the security facilities between systems are unconnected results in an isolated protection structure for systems, and gives rise to a serious challenge to system security integrations and system controls. Also, the need for diversified services and flexible extensions of network security asks for more considerations and contribu?tions from the perspective of software engineering in the process of designing and constructing security systems. Based on the essence of the virtualization technique and the idea of software-defined networks, we in this paper propose a novel software-defi ned security architecture for systems. By abstracting the traditional security facilities and techniques, the proposed security architecture provides a new, simple, effective, and programmable framework in which security operations and security controls can be decoupled, and thereby reduces the software module sizes, decreases the intensity of software deve?lopments, and improves the security extensibility of systems.

关键词： information security networksecurity security architecture software-definedsecurity

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A User Participation Behavior Prediction Model of Social Hotspots Based on Influence and Markov Random Field

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China Communications 2017年第5期14卷 145-159页

作者： Yunpeng Xiao Jiawei Lai Yanbing Liu chongqing engineering laboratory of Network and information security Chongqing University of Posts and TelecommunicationsChongqing 400065China

Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.

关键词： social network hotspot topic behavior prediction Markov random field influence factor

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The internet of Things under Federated Learning:A Review of the Latest Advances and Applications

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Computers, Materials & Continua 2025年第1期82卷 1-39页

作者： Jinlong Wang Zhenyu Liu Xingtao Yang Min Li Zhihan Lyu School of information and Control engineering Qingdao University of TechnologyQingdao260043China Anhui Province Key laboratory of Intelligent Building&Building Energy Saving Anhui Jianzhu UniversityHefei230000China Department of Game Design Uppsala UniversityUppsala75310Sweden

With the rapid development of artificial intelligence,the internet of Things(IoT)can deploy various machine learning algorithms for network and application *** the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious *** learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data *** review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy *** this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy ***,we focus on exploring and analyzing the advantages of the combination of FL on the internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning *** also list various application scenarios of FL on the ***,we propose several open research challenges and possible solutions.

关键词： Federated learning internet of Things sensors machine learning privacy security

DDoS Attack Tracking Using Multi-Round Iterative Viterbi Algorithm in Satellite internet

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China Communications 2025年第03期 148-163页

作者： Guo Wei Xu Jin Pei Yukui Yin Liuguo Feng Wei Department of Electronic engineering Tsinghua University China Telecom Cybersecurity Technology Co. Ltd Operation Center of Network security Products China Telecom Co. Ltd. Beijing National Research Center for information Science and Technology Research Institute of Tsinghua Pearl River Delta

Satellite internet(SI) provides broadband access as a critical information infrastructure in ***, with the integration of the terrestrial internet, the influx of massive terrestrial traffic will bring significant threats to SI, among which DDoS attack will intensify the erosion of limited bandwidth resources. Therefore, this paper proposes a DDoS attack tracking scheme using a multi-round iterative Viterbi algorithm to achieve high-accuracy attack path reconstruction and fast internal source locking, protecting SI from the source. Firstly, to reduce communication overhead, the logarithmic representation of the traffic volume is added to the digests after modeling SI, generating the lightweight deviation degree to construct the observation probability matrix for the Viterbi algorithm. Secondly, the path node matrix is expanded to multi-index matrices in the Viterbi algorithm to store index information for all probability values, deriving the path with non-repeatability and maximum probability. Finally, multiple rounds of iterative Viterbi tracking are performed locally to track DDoS attack based on trimming tracking results. Simulation and experimental results show that the scheme can achieve 96.8% tracking accuracy of external and internal DDo S attack at 2.5 seconds, with the communication overhead at 268KB/s, effectively protecting the limited bandwidth resources of SI.

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