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Multidomain Secure Communication and Intelligent Traffic Detection Model in VANETs

International Journal of Intelligent Systems 2025年第1期2025卷

作者： Qikun Zhang Mengqi Liu Ping Li Junling Yuan Hongfei Zhu School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou 450001 China *** School of Cyber Science and Engineering Southeast University Nanjing 210000 China seu.edu.bd School of Information Security Sanmenxia College of Social Administration Sanmenxia 472000 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing 400065 China ***

Vehicular ad-hoc network (VANET) plays a vital role in the intelligent transportation system. It is crucial to ensure secure communication among entities in the VANET for realizing an efficient transportation system. In this scenario, the current communication scheme is vulnerable to the leakage of private information from entities. The research primarily centers on single-domain vehicular networks, with only a limited number of researchers exploring cross-domain authentication among vehicle entities. Cross-domain communication schemes have received little attention from scholars. Furthermore, there are issues, including the susceptibility of in-vehicle conversations to eavesdropping, the vulnerability of long-distance transmissions to interruptions, and the exposure of wireless networks to traffic attacks. To address these issues, a multidomain secure communication and intelligent traffic detection model in VANET is proposed. This model offers several notable advantages as follows: (1) It employs a key self-verification algorithm for local computation and authentication of entity keys. This approach mitigates the risks of identity impersonation attacks and key leakage results from third-party key escrow. (2) A multidomain communication scheme is devised to categorize vehicle-to-vehicle (V2V) scenarios into intradomain and interdomain, which correspond to situations where the communicating parties are within the same domain and across different domains, respectively. (3) We propose the implementation of new session message encryption algorithms for V2V communication. This involves generating dynamic random keys to ensure secure data sharing and facilitates long-distance cross-domain communication among vehicles. (4) An intelligent two-layer traffic detection paradigm is proposed to improve the efficiency of detecting attack traffic in vehicular networks. This paper provides security proofs and performance analysis of the proposed scheme. The experimental results dem

关键词： Secure communication

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CTF-Net: Retinal Vessel Segmentation via Deep Coarse-To-Fine Supervision Network

CTF-Net: Retinal Vessel Segmentation via Deep Coarse-To-Fine...

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IEEE International Symposium on Biomedical Imaging

作者： Kun Wang Xiaohong Zhang Sheng Huang Qiuli Wang Feiyu Chen School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Retinal blood vessels structure plays an important role in the early diagnosis of diabetic retinopathy, which is a cause of blindness globally. However, the precise segmentation of retinal vessels is often extremely challenging due to the low contrast and noise of the capillaries. In this paper, we propose a novel model of deep coarse-to-fine supervision network (CTF-Net) to solve this problem. This model consists of two U-shaped architecture(coarse and fine segNet). The coarse segNet, which learns to predict probability retina map from input patchs, while the fine segNet refines the predicted map. To gain more paths to preserve the multi-scale and rich deep features information, we design an end-to-end training network instead of multi-stage learning framework to segment the retina vessel from coarse to fine. Furthermore, in order to improve feature representation and reduce the number of parameters of model, we introduce a novel feature augmentation module (FAM-residual block). Experiment results confirm that our method achieves the state-of-the-art performances on the popular datasets DRIVE, CHASE_DB1 and STARE.

关键词： Image segmentation Training Decoding Retinal vessels Biomedical imaging Standards

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Holographic capture and projection system of real object based on tunable zoom lens

引用

PhotoniX 2020年第1期1卷 271-285页

作者： Di Wang Chao Liu Chuan Shen Yan Xing Qiong-Hua Wang School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang UniversityBeijing 100191China Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei 230039China.

In this paper, we propose a holographic capture and projection system of real objectsbased on tunable zoom lenses. Different from the traditional holographic system, aliquid lens-based zoom camera and a digital conical lens are used as key parts to reachthe functions of holographic capture and projection, respectively. The zoom camera isproduced by combing liquid lenses and solid lenses, which has the advantages of fastresponse and light weight. By electrically controlling the curvature of the liquid-liquidsurface, the focal length of the zoom camera can be changed easily. As anothertunable zoom lens, the digital conical lens has a large focal depth and the opticalproperty is perfectly used in the holographic system for adaptive projection, especiallyfor multilayer imaging. By loading the phase of the conical lens on the spatial lightmodulator, the reconstructed image can be projected with large depths. With theproposed system, holographic zoom capture and color reproduction of real objects canbe achieved based on a simple structure. Experimental results verify the feasibility ofthe proposed system. The proposed system is expected to be applied to microprojectionand three-dimensional display technology.

关键词： Spatial light modulator Holographic system Zoom lenses Display technology

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Probabilistic methods for approximate archetypal analysis

arXiv

引用

arXiv 2021年

作者： Han, Ruijian Osting, Braxton Wang, Dong Xu, Yiming Department of Statistics The Chinese University of Hong Kong Hong Kong Department of Mathematics University of Utah Salt Lake City United States School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Scientific Computing and Imaging Institute University of Utah Salt Lake City United States

Archetypal analysis is an unsupervised learning method for exploratory data analysis. One major challenge that limits the applicability of archetypal analysis in practice is the inherent computational complexity of the existing algorithms. In this paper, we provide a novel approximation approach to partially address this issue. Utilizing probabilistic ideas from high-dimensional geometry, we introduce two preprocessing techniques to reduce the dimension and representation cardinality of the data, respectively. We prove that provided the data is approximately embedded in a low-dimensional linear subspace and the convex hull of the corresponding representations is well approximated by a polytope with a few vertices, our method can effectively reduce the scaling of archetypal analysis. Moreover, the solution of the reduced problem is near-optimal in terms of prediction errors. Our approach can be combined with other acceleration techniques to further mitigate the intrinsic complexity of archetypal analysis. We demonstrate the usefulness of our results by applying our method to summarize several moderately large-scale datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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Label-consistency based graph neural networks for semi-supervised node classification

arXiv

引用

arXiv 2020年

作者： Xu, Bingbing Huang, Junjie Hou, Liang Shen, Huawei Gao, Jinhua Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network (LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification. We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

引用

IEEE Network 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Xiangjiang Laboratory Changsha410205 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China University of Concordia Department of Electrical and Computer Engineering Montreal Canada Nanyang Technological University College of Computing and Data Science Singapore Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

In intelligent transportation systems (ITSs), integrating pedestrians and vehicles into traffic management models is essential for developing realistic and safe solutions. However, current systems often fail to simulate complex, real-world scenarios due to the absence of a comprehensive digital twin framework across diverse traffic environments and effective modeling of pedestrian-vehicle interactions. In this article, we propose a surveillance video-assisted federated digital twin (SV-FDT) framework to enhance ITSs by incorporating pedestrians and vehicles into the control loop. SV-FDT improves computational efficiency and communication performance by transmitting only semantic data and agent parameters, rather than raw video streams. The proposed framework adopts three-layer architecture and constructs detailed pedestrian-vehicle interaction models using multi-source traffic surveillance videos. The three-layer architecture includes: (i) an end layer that collects surveillance videos from multiple sources;(ii) an edge layer that performs self-supervised semantic segmentation to extract interactions, converts them into executable traffic codes, and generates local digital twin systems (LDTSs) for regional traffic modeling;and (iii) a cloud layer that integrates LDTSs into a real-time global digital twin model. key design considerations, challenges, and practical implementation guidelines are discussed for SV-FDT, and a testbed evaluation is used to show that SV-FDT improves traffic flow, reduces mirroring delay, and enhances recognition accuracy and system efficiency compared to traditional terminal-server frameworks. Finally, we outline open challenges and potential directions for future research in digital twin-enabled ITS. © 1986-2012 IEEE.

关键词： Air traffic control

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Analysis of 5G academic Network based on graph representation learning method

arXiv

引用

arXiv 2021年

作者： Li, Xiaoming Xu, Guangquan Yu, Wei Jiao, Pengfei Song, Xiangyu Zhejiang Yuexiu University of Foreign Languages School of International Business Shaoxing China Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart City Shaoxing China School of Big Data School of Big Data Qingdao Huanghai University Qingdao China College of Intelligence and Computing Tianjin University Tianjin China School of IT Faculty of Science Engineering and Built Environment Deakin University GeelongVIC3220 Australia

With the rapid development of 5th Generation Mobile Communication Technology (5G), the diverse forms of collaboration and extensive data in academic social networks constructed by 5G papers make the management and analysis of academic social networks increasingly challenging. Despite the particular success achieved by representation learning in analyzing academic and social networks, most present presentation learning models focus on maintaining the first-order and second-order similarity of nodes. They rarely possess similar structural characteristics of spatial independence in the network. This paper proposes a Low-order Network representation Learning Model (LNLM) based on Non-negative Matrix Factorization (NMF) to solve these problems. The model uses the random walk method to extract low-order features of nodes and map multiple components to a low-dimensional space, effectively maintaining the internal correlation between members. This paper verifies the performance of this model, conducts comparative experiments on four test datasets and four real network datasets through downstream tasks such as multi-label classification, clustering, and link prediction. Comparing eight mainstream network representation learning models shows that the proposed model can significantly improve the detection efficiency and learning methods and effectively extract local and low-order features of the network. © 2021, CC BY.

关键词： Non-negative matrix factorization

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Radiomics based on dual-energy CT for noninvasive prediction of cervical lymph node metastases in patients with nasopharyngeal carcinoma

引用

Radiography 2025年第4期31卷 102989-102989页

作者： Li, L. Yang, D. Wu, Y. Sun, R. Qin, Y. Kang, M. Deng, X. Bu, M. Li, Z. Zeng, Z. Zeng, X. Jiang, M. Chen, B.T. Department of Radiology First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Radiology Guizhou Provincial People Hospital No.83 East Zhongshan Road Nanming District Guizhou Province Guiyang 550000 China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University No. 2870 Huaxi Avenue South Guizhou Guiyang 550025 China Department of Nuclear Medicine First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Radiation Oncology First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Diagnostic Radiology City of Hope National Medical Center Duarte CA United States

Introduction: To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node metastases (CLNM) in patients diagnosed with nasopharyngeal carcinoma (NPC). Methods: This prospective single-center study enrolled patients with NPC and the study assessment included both DECT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Radiomics features were extracted from each region of interest (ROI) for cervical lymph nodes using arterial and venous phase images at 100 keV and 150 keV, either individually as non-fusion models or combined as fusion models on the DECT images. The performance of the random forest (RF) models, combined with radiomics features, was evaluated by area under the receiver operating characteristic curve (AUC) analysis. DeLong's test was employed to compare model performances, while decision curve analysis (DCA) assessed the clinical utility of the predictive models. Results: Sixty-six patients with NPC were included for analysis, which was divided into a training set (n = 42) and a validation set (n = 22). A total of 13 radiomic models were constructed (4 non-fusion models and 9 fusion models). In the non-fusion models, when the threshold value exceeded 0.4, the venous phase at 100 keV (V100) (AUC, 0.9667;95 % confidence interval [95 % CI], 0.9363–0.9901) model exhibited a higher net benefit than other non-fusion models. The V100 + V150 fusion model achieved the best performance, with an AUC of 0.9697 (95 % CI, 0.9393–0.9907). Conclusion: DECT-based radiomics effectively diagnosed CLNM in patients with NPC and may potentially be a valuable tool for clinical decision-making. Implications for practice: This study improved pre-operative evaluation, treatment strategy selection, and prognostic evaluation for patients with nasopharyngeal carcinoma by combining DECT and radiomics to predict cervical lymph node status prior to treatment. © 2025 The Co

关键词： Cervical lymph node metastases (CLNM) Dual-energy computed tomography (DECT) Machine learning model Nasopharyngeal carcinoma (NPC) Radiomics

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Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning

arXiv

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arXiv 2022年

作者： Ma, Chuan Li, Jun Wei, Kang Liu, Bo Ding, Ming Yuan, Long Han, Zhu Poor, H. Vincent Zhejiang Laboratory Hangzhou311121 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210096 China The Department of Computing Hong Kong Polytechnic University 999077 Hong Kong The University of Technology SydneyNSW2007 Australia Data61 CSIRO NSW2015 Australia The School of Computer Science Nanjing University of Science and Technology Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Motivated by the advancing computational capacity of distributed end-user equipment (UE), as well as the increasing concerns about sharing private data, there has been considerable recent interest in machine learning (ML) and artificial intelligence (AI) that can be processed on distributed UEs. Specifically, in this paradigm, parts of a ML process are outsourced to multiple distributed UEs. Then the processed information is aggregated on a certain level at a central server, which turns a centralized ML process into a distributed one, and brings about significant benefits. However, this new distributed ML paradigm raises new risks of privacy and security issues. In this paper, we provide a survey on the emerging security and privacy risks of distributed ML from a unique perspective of information exchange levels, which are defined according to the key steps of a ML process, i.e.: i) the level of pre-processed data, ii) the level of learning models, iii) the level of extracted knowledge and, iv) the level of intermediate results. We explore and analyze the potential of threats for each information exchange level based on an overview of the current state-of-the-art attack mechanisms, and then discuss the possible defense methods against such threats. Finally, we complete the survey by providing an outlook on the challenges and possible directions for future research in this critical area. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

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