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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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Discrete Jaya algorithm to solve the police resource deployment problem in emergency traffic accident 10

Discrete Jaya algorithm to solve the police resource deploym...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Jing, Zhang Weijie, Ding Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Aiming at the high frequency of urban traffic accidents and the relative shortage of police resources, a multi-objective discrete Jaya algorithm is proposed to solve the problem of police resource deployment in emergency traffic accident handling. The purpose is to make timely response and handling of traffic accidents through the rational allocation of police deployment points in the jurisdiction. Taking the coverage of accident points and the average response time of accidents as the optimization objectives, the discrete Jaya algorithm is used as the global search strategy to approach the Pareto front by constantly approaching the optimal solution and far away from the worst solution, and then the neighborhood search strategy is used to improve the local search ability of the algorithm. Finally, a practical example is given to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Law enforcement

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Digital Twins in the IIoT:Current Practices and Future Directions Toward Industry 5.0

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Computers, Materials & Continua 2025年第6期83卷 3675-3712页

作者： Bisni Fahad Mon Mohammad Hayajneh Najah Abu Ali Farman Ullah Hikmat Ullah Shayma Alkobaisi Computer&Network Engineering United Arab Emirates UniversityAl Ain15551United Arab Emirates Big Data Analytics Centre United Arab Emirates UniversityAl Ain15551United Arab Emirates Department of Business Information Technology Liwa CollegeAbu Dhabi41009United Arab Emirates Information System&Security United Arab Emirates UniversityAl Ain15551United Arab Emirates

In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry *** article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real *** discusses the emergence of DT,contextualizing its evolution within the framework of *** study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the *** paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and *** emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing *** provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.

关键词： Digital twin in industry integration of DT in IIoT artificial intelligence AI-Driven DT applications

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises’ demand for dynamic multidimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

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

作者： Cai, Jiati Liu, Xiaogang Yang, Hongjie Ding, Yi Zhong, Ting Qin, Zhen Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet. © 2025 IEEE.

关键词： colonoscopy computer vision polyp segmentation representation enhancement

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Privacy-preserving Searchable Encryption Based on Anonymization and Differential privacy

Privacy-preserving Searchable Encryption Based on Anonymizat...

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IEEE International Conference on Web Services (ICWS)

作者： Caixia Ma Chunfu Jia Ruizhong Du Guanxiong Ha Mingyue Li College of Cyber Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin China School of Cyber Security and Computer Hebei University Baoding China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the rapid development of cloud computing, more and more users are storing sensitive data on cloud servers, making the privacy-preserving of data particularly important. Dynamic searchable symmetric encryption enables efficient retrieval of encrypted data in cloud computing environments while preserving data privacy. However, existing solutions are not effective in defending against various query-recovery attacks. Therefore, this paper focuses on the privacy-preserving of dynamic searchable symmetric encryption, and proposes a privacy-preserving dynamic searchable symmetric encryption based on anonymization and differential privacy – DADP. Firstly, the original indexes are synthesized into fake indexes using the anonymization hash technology. The synthetic indexes possess randomness and irreversibility, making it impossible for adversaries to infer the generation process of the synthetic indexes or recover the original indexes. Additionally, by using differential privacy to process composite indexes, the privacy of keywords and index information is protected, preventing adversaries from inferring sensitive information based on query results. This approach provides dual privacy-preserving. Compared to other schemes, our scheme achieves type-I backward privacy and can withstand seven types of query recovery attacks. And it improves update and query efficiency by 10-100 times.

关键词： Differential privacy Privacy Cloud computing Costs Web services Information filtering Encryption Indexes Servers Information integrity

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HANE-SHC: Heterogeneous attributed network embedding with structural homophily contents

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Applied Intelligence 2025年第10期55卷 1-40页

作者： Fu, Yue Zhao, Shuliang Wu, Yongliang College of Information Engineering Hebei GEO University Hebei China College of Computer and Cyber Security Hebei Normal University Hebei China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei China Hebei Provincial Key Laboratory of Network and Information Security Hebei China School of Information Science and Technology Shijiazhuang Tiedao University Hebei China

Heterogeneous attributed networks (HANs) are ubiquitous in real-world, spanning domains from the academic network to the movie network. Heterogeneous attributed network embedding (HANE), a promising analytical technique in recent years, aims to generate low-dimensional dense vectors containing network structure and attribute content. Compared to the embedding approaches that only consider topology structure, researchers embed the network by leveraging both network topology and content to improve the quality of embedding vectors. However, when two nodes with a connected edge may have a small content similarity value, while two nodes without a connected edge may have a large content similarity value, so, in this situation, it is not appropriate to utilize the network topology and nodes’ content to generate embedding vectors. To overcome this challenge, we propose a novel approach of Heterogeneous Attributed networks Embedding based on Structural Homophily Contents, abbreviated as HANE-SHC, which is based on convolutional neural networks to leverage both the network structure and the content of nodes connected with the embedded node to generate the representation vectors of the embedded node. To our knowledge, this is the first study that proposes the Structural Homophily Contents to enhance the heterogeneous attributed network embedding. Through comprehensive evaluations on downstream tasks including clustering, classification, link prediction, and visualization, the experimental results demonstrate that the HANE-SHC outperforms the baselines. On the artificial synthetic networks with various network structure-content dissimilarity scores, the HANE-SHC model consistently produces high-quality embedding vectors regardless of the network dissimilarity scores between structure and content, significantly outperforming existing approaches.

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A network security Situation Assessment Method Based on Multi-attention Mechanism and HHO-ResNeXt 8th

A Network Security Situation Assessment Method Based on Mult...

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8th International Symposium on security and Privacy in Social networks and Big data, SocialSec 2022

作者： Zhao, Dongmei Ji, Guoqing Zeng, Shuiguang School of College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9789811972416

Because the traditional convolutional neural network (CNN) cannot obtain the importance of each channel and its receptive field is limited, it is difficult to deal with the increasingly complex network environment. Aiming at the shortcomings, this paper combines the Contextual Transformer (COT) block and the Efficient Channel Attention (ECA) module with ResNeXt and uses Harris Hawks Optimization (HHO) algorithm to choose the most suitable hyperparameters to improve the model performance. This model enables the rich contexts among neighbor keys to be fully exploited and can obtain the importance of each channel to improve the weight of the useful channel and suppress the less useful channel, effectively making up for the shortcomings of the traditional convolutional neural network. The experiments on a network security dataset show that the model is superior to other models in the network security situation assessment effect and its comprehensive performance in accuracy, precision and F-score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Design and Implementation of Cyber Space Threat Detection System Based on User Behavioral Logs 3

Design and Implementation of Cyber Space Threat Detection Sy...

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Wang, Wenhao Li, Hao Nie, Peng College of Cyber Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Science and Technology on Communication Networks Laboratory Shijiazhuang China College of Cyber Science Nankai University Tianjin China

ISBN: (纸本)9798350359961

In contemporary society, the role of networks in daily work and business activities is increasingly prominent. The enterprise intranet, serving as a crucial bridge for maintaining work stability, asset security, and business continuity, has garnered significant attention from both academia and industry in the field of intranet security detection. This study is based on the Log2Vec algorithm and involves the design and implementation of a Cyber Space Threat Detection System. The system, facilitated by the collaborative efforts of three modules-log ingestion, graph encoding, and security detection-effectively showcases user behavior, encodes logs into a heterogeneous graph, transforms the graph into vectors, and conducts cluster analysis to detect abnormal user behavior. Through in-depth research and systematic design, this study aims to provide enterprises with a more advanced and comprehensive intranet security detection solution to cope with evolving cybersecurity threats. © 2023 IEEE.

关键词： Cybersecurity Heterogeneous Graph Log2Vec

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Three-level Compact Caching for Search Engines Based on Solid State Drives 23

Three-level Compact Caching for Search Engines Based on Soli...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Rui Sun, Pengyu Tong, Jiancong Zang, Ruirui Qian, Heng Pan, Yu Stones, Rebecca J. Wang, Gang Liu, Xiaoguang Li, Yusen College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Baidu Inc Beijing China

ISBN: (纸本)9781665494571

The query processing in search engines can be classified into either disk I/O operations and CPU computation operations. Conventional search engine caching methods mostly aimed at reducing disk I/O operations. However, with the new trend of using solid state drives (SSDs) instead of hard disk drives for search engine storage, the bottleneck of query processing shifts from disk I/O to computation. In this paper, we design a three-level compact caching structure suited to SSD-based search engines, incorporating caches for (a) doculets, partial documents which contain previously encountered snippets, (b) fragments, compact data structures containing snippet metadata, and (c) ranked docID lists, which index the top-k documents relevant to a query. This three-level compact caching method aims at reducing the bottleneck of repetitive CPU computation operations. Moreover, by considering different levels of caches as a whole system, the proposed three-level compact caching eliminates the redundancy among cache items. This increases the efficiency of cache space utilization. Experimental results indicate an improvement in query response latency by a factor of around 2.6 when using static caching and 1.3 under dynamic caching. © 2021 IEEE.

关键词： Costs Solid state drives Smart cities Query processing Redundancy Search engines Metadata

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