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Zero Trust Networks: Evolution and Application from Concept to Practice

computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

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computer Modeling in engineering & sciences 2024年第2期138卷 1365-1384页

作者： Dun Cao Jia Ru Jian Qin Amr Tolba Jin Wang Min Zhu College of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time *** modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is *** paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

关键词： Internet of vehicles road networks 3D road model structure recognition GIS

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An effective image fusion algorithm based on grey relation of similarity and morphology

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14859-14872页

作者： Liu, Caiping Long, Yahui Mao, Jianxu Zhang, Hui Huang, Ruizhi Dai, Yang College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Electrical and Information Engineering Hunan University Changsha410082 China College of Electrical and Information Engineering Changsha University of Science and Technology Changsha410012 China

Considering the problems of the limited energy in wireless multi-media sensor networks (WMSNs) and the focused regions discontinuity of the fused image obtained using traditional multi-scale analysis tools (MST)-based methods, an effective multi-focus image fusion algorithm is proposed in this paper. In this method, the original fused image is obtained based on wavelet transform where the low-frequency coefficients are fused by average scheme, whereas the high-frequency coefficients are fused by the proposed merging rule consisting of the grey relation analysis of similarity and local area energy. Then, grey absolute relation analysis is again utilized as measurement indicator to estimate the similarities between the initial fused image and source images, during which the initial map is acquired and then corrected by the mathematical morphological opening and closing. Finally, the fused image is obtained with the guidance of the corrected map, namely the decision map. Experiment results demonstrate that the fused image using the proposed algorithm is more continuous in focused region and more similar to source images in brightness compared with state-of-art multi-focus image fusion algorithms, such as Curvelet transform, lifting stationary wavelet transform (LSWT), non-subsampled contourlet transform (NSCT) and non-subsampled shearlet transform (NSST). Meanwhile, the proposed method shows better superiority in term of the computational complexity. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Computational complexity

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Accelerating Point Clouds Classification in Dynamic Graph CNN with GPU Tensor Core 29

Accelerating Point Clouds Classification in Dynamic Graph CN...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Huang, Haocheng Li, Yangfan Zhou, Xinlian Hunan University of Science and Technology The School of Computer Science and Engineering Xiangtan China Central South University The School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350330717

Point clouds play a crucial role in various fields such as robotics, 3D modeling, and autonomous driving. DGCNN, as a representative work in this domain, has exhibited superior performance compared to classic models like PointNet++. Such models often display significant computational demands and encounter notable performance bottlenecks, so how to accelerate their calculations has become a noteworthy issue. Nevertheless, due to the inherent disparities between point cloud tasks and conventional convolution tasks, existing algorithms and architectural paradigms designed for convolution frequently prove to be ill-suited for point clouds, such as Tensor Core (TC). There have been studies focused on reducing DGCNN model complexity to improve computational efficiency, but research specifically tailored to TC acceleration remains relatively limited. Therefore, we present an encapsulation module that bridges the computation of model in Torch level with the components of the CUDA backend, introducing TC into DGCNN. It dynamically adjusts parameters and computation flow based on input data and transforms batch computations into streaming computations. Additionally, we propose a new computational organization approach, which involves the restructuring of two critical computation steps within DGCNN: the Get Graph Feature operation and the Conv operation. It significantly enhances the computational speed of these two operations while reducing memory redundancy of tensor data involved in the intermediate processes. In the experiment, our modifications achieve an average acceleration of 1.80X for the Get Graph Feature operation and 1.46X for the Conv operation, all while not compromising result accuracy. © 2023 IEEE.

关键词： Classification Convolution Dynamic graph update GPU Neural Networks Point Cloud Tensor Core

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A bi-layer coordinated power regulation strategy considering system dynamics and economics for isolated hybrid AC/DC multi-energy microgrid

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science China(Technological sciences) 2025年第1期68卷 374-390页

作者： Jing ZHANG Yong LI Jinjie LIN Sijia HU Yijia CAO Li HE Xusheng YANG Yong XU Lin ZENG Linjin XIE College of Electrical and Information Engineering Hunan UniversityChangsha 410082China College of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China State Grid Integrated Energy Service Group Co. Ltd.Beijing 101207China State Grid Hunan Integrated Energy Service Co. Ltd.Changsha 410000China Hunan Disaster Prevention Technology Co. Ltd.Changsha 410000China

The isolated hybrid AC/DC multi-energy microgrid(IH-MEMG)offers an effective solution for meeting the electrical,heating,and cooling energy demands of remote and off-grid *** an IH-MEMG,system transient dynamics(i.e.,frequency or voltage of the electricity network)and economics are critical aspects that pose the greatest concern to ***,these aspects are generally investigated separately owing to their different time *** integrate these aspects from the scope of real-time control,this study proposes a bi-layer coordinated power regulation strategy considering system dynamics and economics for the ***,coupling relationships among multiple sub-networks are analyzed,and a frequency-voltage coupling model between the AC and DC sides is ***,a bi-layer coordinated power regulation strategy is developed for the IH-MEMG with output characteristics of different components involved:the primary layer includes a multi-entity power support mechanism used to improve the dynamics of the electricity network,wherein a cooperation principle of the combined cooling,heating,and power(CCHP)unit and energy storage unit(ESU)is designed in detailed;meanwhile,the secondary layer includes a real-time economics-oriented optimization framework used to adjust the power references of multiple units generated from the primary layer for cost efficiency improvement(notably,the primary layer can work independently).Finally,the effectiveness of the proposed strategy is verified through comprehensive simulations under varying operation scenarios.

关键词： isolated hybrid AC/DC multi-energy microgrid bi-layer coordinated power regulation dynamics and economics combined cooling heating and power unit energy storage unit

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A deep graph convolution network with attention for clustering scRNA-seq data

A deep graph convolution network with attention for clusteri...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Xiang Yu, Junnan Peng, Li Li, Min Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Identifying cell types is a primary objective in single-cell RNA sequencing (scRNA-seq) analysis, and clustering is a widely used method for achieving this goal. However, the abundance of data and the presence of noise pose significant challenges for single-cell clustering. We propose a novel method called scGCNClustering, which leverages a deep graph convolution network (GCN) with attention. scGCNClustering comprises two core models. The first model is a deep GCN encoder that effectively preserves both the topological structure and feature information in scRNA-seq data, enabling accurate cell segregation. Additionally, we incorporate an attention module into the GCN encoder to capture precise global similarities between cells. The second model is a deep zero-inflated negative binomial (ZINB) decoder, which approximates the true distribution of scRNA-seq data. Extensive analysis conducted on six real scRNA-seq datasets demonstrates that scGCNClustering achieves promising performance in scRNA-seq clustering. © 2023 IEEE.

关键词： Clustering Graph convolution network Single cell RNA-seq

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Dear: vehicle mobility prediction using diffusion-expanded attention network based on IoV trajectory data

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Neural Computing and Applications 2024年 1-15页

作者： Yang, Jiali Yang, Kehua Zeng, Fanzi Cheng, Qixuan Xiao, Zhu Jiang, Hongbo College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Chongqing Research Institute of Hunan University Chongqing404100 China

In recent years, the rapid development of vehicle networking technology makes the acquisition of Internet of vehicles (IoV) trajectory data easier and more extensive. Mobility prediction based on these IoV trajectory data has become one of the research hotspots in the field of transportation. However, due to the randomness and nonlinear characteristics of IoV trajectory data, vehicle mobility prediction is a challenging task. In addition to traditional statistical methods, neural networks are promising methods for predicting vehicle mobility. Therefore, we design a deep neural network model and introduce the attention mechanism to achieve accurate prediction of vehicle mobility by learning and modeling the timing characteristics of IoV trajectory data. Diffusion-Expanded Attention Recurrent Neural Network (DEAR), a position prediction model for preference perception, is proposed. The introduction of diffusion model can better consider the dynamics of vehicle movement, adapt to the ever-changing traffic environment, and improve the accuracy of trajectory prediction. The experimental results show that our method can accurately predict the moving behavior of vehicles such as position, speed, and acceleration, which provides important support for real-time traffic flow optimization and driving safety, and has a wide application prospect. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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A One-Dimensional Residual Network and Physical Fitness-Based Exercise Prescription Recommendation Method 10

A One-Dimensional Residual Network and Physical Fitness-Base...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Fan, Runqing Peng, Zhenlian Cao, Buqing Liu, Jianxun Che, Peng Chen, Tieping Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In the context of the national big data strategy, physical fitness test data has become one of the main influencing factors in guiding and promoting the participation of the population in sports and fitness. Recommending exercise prescriptions based on national physical fitness test data has become an important research topic. However, currently, there is little research on how to accurately use computer data processing technology to recommend exercise prescriptions based on physical fitness test data. In this study, we propose a ResNet-based Exercise Prescription (ResNet-EP) method that utilizes one-dimensional residual neural network technology to recommend exercise prescriptions based on physical fitness testing data. This method comprehensively analyzes physical fitness testing data and exercise prescription data and realizes the automatic recommendation of exercise prescriptions. Experimental results on a real dataset demonstrate that the ResNet-EP model outperforms other comparison models in terms of precision (79.98%), recall (83.73%), and F1 score (81.81%). This study provides novel insights into the combination of physical fitness testing and exercise. © 2023 IEEE.

关键词： Machine learning

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A missing physical fitness test data classification method based on MLP 10

A missing physical fitness test data classification method b...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Che, Peng Peng, Zhenlian Cao, Buqing Liu, Jianxun Chen, Tieping Fan, Runqing Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %. © 2023 IEEE.

关键词： Classification (of information)

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Injecting Visual Features into Whisper for Parameter-Efficient Noise-Robust Audio-Visual Speech Recognition

Injecting Visual Features into Whisper for Parameter-Efficie...

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

作者： Yang, Zhao Yeo, Yue Heng Jiang, Rui Fu, Xiao Chen, Weiguang Xi, Wei Zhao, Jizhong School of Computer Science and Technology Xi'an Jiaotong University China School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)9798350368741

Audio-visual speech recognition (AVSR) aims to enhance the robustness of an automatic speech recognition (ASR) systems by incorporating visual information from lip movements, especially in challenging noisy environments. Nevertheless, most current approaches either involve training from scratch or fully finetuning a pre-trained model, both of which incur significant computational costs and are often impractical for large-scale speech foundation models. This gap highlights the need for more efficient methods to leverage visual and acoustic information in AVSR tasks. To address this challenge, we propose AVWhisper, a parameter-efficient model that integrates visual and acoustic representations by injecting visual features from the AV-HuBERT encoder into the pre-trained Whisper model. Our approach leverages the existing attention mechanisms in Whisper to facilitate cross-modal interaction and integrates auxiliary visual information through lightweight adapters based on Low-Rank Adaptation (LoRA) and prompt-based techniques. Furthermore, a two-phase training strategy is adopted to effectively handle cross-domain differences and visual information injection problems respectively. Extensive experiments on the LRS3-TED dataset demonstrate that AVWhisper consistently outperforms state-of-the-art methods across various noise conditions, offering a more efficient and scalable solution for audio-visual speech recognition. © 2025 IEEE.

关键词： Audio-Visual Speech Recognition LoRA Noise-Robust Parameter-Efficient Prompt Whisper

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