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Online Flow Scheduling in Virtualized Time-Sensitive networks: A Joint Admission Control and VNF Embedding Approach

IEEE Transactions on Mobile Computing 2025年第06期24卷 4651-4667页

作者： Zhang, Yajing Chen, Cailian Li, Mingyan You, Chaoqun Guan, Xinping Quek, Tony Q. S. Shanghai Satellite Network Research Institute Co. Ltd. Shanghai200120 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Chongqing University College of Computer Science Chongqing400044 China Fudan University School of Computer Science Shanghai200433 China Singapore University of Technology and Design 487372 Singapore

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission Control (SmartAC), to cater to dynamic applications. The first stage, referred to as the static stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as the dynamic stage, fine-tunes VNE solutions obtained from the static stage to adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies. © 2002-2012 IEEE.

关键词： Budget control

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Skyrmionic spin textures in nonparaxial light

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Advanced Photonics 2025年第1期7卷 85-92页

作者： Xinrui Lei Aiping Yang Xusheng Chen Luping Du Peng Shi Qiwen Zhan Xiaocong Yuan Shenzhen University Institute of Microscale Optoelectronics and State Key Laboratory of Radio Frequency Heterogeneous IntegrationNanophotonics Research CenterShenzhen Key Laboratory of Micro-Scale Optical Information TechnologyShenzhenChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringShanghaiChina Dongguan University of Technology Research Institute of Interdisciplinary Sciences and School of Materials Science and EngineeringDongguanChina Research Centre for Frontier Fundamental Studies Zhejiang LabHangzhouChina

Topological textures in optics such as skyrmions and merons are increasingly studied for their potential functions in light–matter interactions,deep-subwavelength imaging,and ***,they were previously generated either in strongly confined guided waves or in paraxial *** has posed a significant challenge in constructing skyrmions in nonparaxial propagating waves due to the lack of symmetry-breaking in the optical field and difficulty in characterizing the full three-dimensional spin textures at the *** theoretically propose and experimentally demonstrate the generation of skyrmionic spin textures in nonparaxial light,where skyrmionic textures with a Bloch-type scheme,including isolated skyrmioniums,skyrmion,and meron lattices are generated in free *** introduce the interplay between the Hertz potentials to break the dual symmetry of light and build well-defined domains of *** experimentally realized the topological textures by applying a hybrid polarized optical vortex and observed the complete three-dimensional spin distributions by a dual-mode waveguide *** bridging the gap in the skyrmionic group,we present a topologic diagram,showing how spin–orbit coupling of light governs the spin *** findings offer new insights into optical quasiparticles and electron–photon correspondence,potentially facilitating advanced applications in optical metrology,sensing,and storage.

关键词： spin topology photonic skyrmion spin–orbit coupling of light

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A two Stage Mixed Integer Programming Model for Distributionally Robust State Based Non-Intrusive Load Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Chen Chai, Zimo Yang, Linfeng University of Shanghai for Science and Technology School of Mechanical Engineering Shanghai China Guangxi University School of Computer Electronics and Information the Guangxi Key Laboratory of Multimedia Communication and Network Technology Nanning China

This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this paper model introduces fewer integer variables and richer absolute error function of load decomposition, which makes the power state selection of each device more accurate and the power consumption more accurate fitting. First, to tackle the issues related to noisy load data, an innovative load feature extraction model based on Kullback-Leibler distributionally robust optimization principles is introduced. Then the key features (power boundary/fluctuation features) of each device identified through this robust model are integrated into the constraints of the two-stage NILM model. The two-stage complementary framework includes: the determination of device state interval in the first stage;and the accurate fitting of device power consumption within the device state interval in the second stage. Comparative validation against existing optimization-based models on the AMPds, REFIT, and actual laboratory data sets demonstrate that our proposed model significantly enhances power decomposition accuracy and computational efficiency. In addition, the two-stage complementary framework and load feature extraction model can be applied to other optimization-based models of NILM to improve the computational efficiency of each model and the accuracy of load decomposition. © 2025 IEEE.

关键词： Mixed-integer linear programming

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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

作者： Ni, Jinghui Cui, Hui Zhao, Lihai Li, Fengling Han, Xiaohui Xu, Lijuan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Quan Cheng Laboratory China Shandong Open University China University of Technology Sydney Australia

ISBN: (纸本)9798350368741

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA. © 2025 IEEE.

关键词： Deep hashing image retrieval source-free domain adaptation

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Crop Classification Methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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ENS-RFMC: An Encrypted network Traffic Sampling Method Based on Rule-Based Feature Extraction and Multi-hierarchical Clustering for Intrusion Detection 27th

ENS-RFMC: An Encrypted Network Traffic Sampling Method Based...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Liang-Chen Gao, Shu Wei, Zi-Xuan Liu, Bao-Xu Zhang, Xu-Yao School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Institute of Automation NLPR Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer China University of Labor Relations Beijing100048 China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9783031783821

Efficiently and quickly identifying malware in encrypted network traffic is a major challenge. This research presents and evaluates a novel approach for sampling encrypted network traffic, called ENS-RFMC. Initially, the method employs a rule-based strategy for extracting relevant metadata features of encrypted traffic from network connections, SSL and certificates log files. Subsequently, a multi-hierarchical clustering algorithm is utilized to divide the dataset into smaller clusters, with benign clusters subsequently eliminated for streamline data processing. Classifier models such as Random Forests, XGBoost and SVM are then utilized to detect attacks and assess performance. Experimental results indicate that the proposed ENS-RFMC sampling method is effective, with the encrypted traffic intrusion detection model employing ENS-RFMC sampling exhibits enhanced accuracy and efficiency in identifying attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： network intrusion

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A Category-Driven Contrastive Recovery network for Double Incomplete Multi-view Multi-label Classification

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IEEE Transactions on Multimedia 2025年

作者： Wang, Yiming Li, Qun Chang, Dongxia Wen, Jie Xiao, Fu Zhao, Yao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China

In the field of multi-view multi-label learning, the challenges of incomplete views and missing labels are prevalent due to the complexity of manual labeling and data acquisition errors. These challenges significantly reduce the quality of latent representations and hinder prediction by multi-label classification. To address this issue, we propose a novel Category-driven Semi-supervised Contrastive Recovery (CSCR) framework in this study. Our framework aims to fully integrate existing label information into incomplete representation learning and classification. Specifically, to address the limitations posed by incomplete views and labels, we construct a label coincidence matrix based on existing labels, which serves as a similarity matrix in subsequent semi-supervised contrastive learning and multi-view classification. By leveraging this matrix, we design a semi-supervised multi-view contrastive learning module, which constructs sample pairs on the basis of inter-view correspondences and label similarity. It learns discriminative latent representations without the need for data augmentation. A weighted multi-label classification module is subsequently employed to integrate the predictions from each view to obtain the final classification result. Experimental evaluations on five challenging datasets demonstrate the superiority of our model over existing state-of-the-art methods. © 1999-2012 IEEE.

关键词： Contrastive Learning

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Verifiable and Forward-Secure Multi-keyword Query in Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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TLDP: An Algorithm of Local Differential Privacy for Tensors

arXiv

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arXiv 2025年

作者： Yuan, Yachao Tang, Xiao Huang, Yu Wang, Jin School of Future Science and Engineering Soochow University Jiangsu Suzhou China School of Mathematics Southeast University Jiangsu Nanjing China School of Cyber Science and Engineering Southeast University Jiangsu Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Tensor-valued data, increasingly common in applications like spatiotemporal modeling and social networks, pose unique challenges for privacy protection due to their multidimensional structure and the risk of losing critical structural information. Traditional local differential privacy (LDP) methods, designed for scalars and matrices, are insufficient for tensors, as they fail to preserve essential relationships among tensor elements. We introduce TLDP, a novel LDP algorithm for Tensors, which employs a randomized response mechanism to perturb tensor components while maintaining structural integrity. To strike a better balance between utility and privacy, we incorporate a weight matrix that selectively protects sensitive regions. Both theoretical analysis and empirical findings from real-world datasets show that TLDP achieves superior utility while preserving privacy, making it a robust solution for high-dimensional data. © 2025, CC BY-SA.

关键词： Differential privacy

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UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning 27th

UAD-DPL: An Unknown Encrypted Attack Detection Method Based ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Liang-Chen Gao, Shu Liu, Bao-Xu Zhang, Xu-Yao School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Institute of Automation NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer China University of Labor Relations Beijing100048 China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9783031781681

In the field of cybersecurity, a significant concern is the potential for unknown attacks to occur at any moment. The primary obstacle faced by intrusion detection systems lies in the identification of unknown attacks and the differentiation of such threats from benign network traffic and known attack patterns. Many current deep learning approaches struggle to discern the underlying features of these new attack types, especially in the case of encrypted attacks, resulting in inherent inaccuracies. To tackle these challenges, a novel unknown attack detection method based on deep prototype learning (UAD-DPL) is proposed to improve the accuracy of unknown encrypted attack detection. Firstly, a spatiotemporal fusion feature extraction network for encrypted traffic is employed to enhance the feature representation ability. Subsequently, an innovative prototype-based deep feature space learning model is proposed, which utilizes discriminative loss and open loss training to ensure that the learned encrypted traffic features meet the predetermined prior distribution. Finally, an unknown attack recognition approach is designed to effectively identify both known and unknown attacks, and a three-stage training strategy is proposed to train the model. Experimental results indicate that the UAD-DPL method is highly proficient in detecting both known and unknown attacks in encrypted traffic. Moreover, UAD-DPL exhibits promising application potential in open-set network intrusion detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： network intrusion

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