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International Conference on Computer Supported Cooperative Work in Design

作者： Zixuan Chen Chao Zheng Zhao Li Jinqiao Shi Zeyu Li School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China Geedge Networks Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Stepping-stones are widely used by attackers to conceal their identities and gain unauthorized access to restricted targets. Numerous strategies have been suggested to identify stepping-stones and counteract evasive behaviors, with flow correlation standing out as the key technique used in many deanonymization methods. Existing attempts to tackle the flow correlation issue rely on long-flow observations and feature-based methods. Yet, these approaches meet substantial limitations in their suitability across diverse scenarios, network noises influence and accuracy, particularly in the stepping-stone environment. In this paper, we introduce an improved flow correlation model, FlowLinker, which aims to resolve these issues. Specifically, we combined multidimensional statistical features and cumulative flow sequences to construct a robust traffic feature. Then, we leveraged the triplet network to produce an optimized representation, amplifying the difference between unrelated representations. Consequently, it significantly reduces the false correlation rate with flows that seem similar but are unrelated. The experiments on real-world datasets from different network environments we collected show that FlowLinker outperforms other state-of-the-art methods with shorter lengths of flow observations.

关键词： Correlation Accuracy Federated learning Computational modeling Noise

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Practical privacy-preserving mixing protocol for Bitcoin

Practical privacy-preserving mixing protocol for Bitcoin

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International Conference on Computer Supported Cooperative Work in Design

作者： Qianqian Chang Lin Xu Lei Zhang Shanghai Key Laboratory of Trustworthy Computing East China Normal University China Science and Technology on Communication Security Laboratory Chengdu China Ministry of Education Engineering Research Center of Software/Hardware Co-Design Technology and Application China

The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. Compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies.

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Dam regulation on extreme streamflow-sediment relationships along the Yangtze River Basin

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Science Bulletin 2025年第6期70卷 866-870页

作者： Guangyao Gao Anqi Huang Lishan Ran Dongfeng Li Hong Zhang Yadu Pokhrel Liqiang Yao Bojie Fu State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China State Key Laboratory of Environmental Aquatic Chemistry Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China University of Chinese Academy of Sciences Beijing 100049China Department of Geography The University of Hong KongHong Kong 999077China Key Laboratory for Water and Sediment Sciences Ministry of EducationCollege of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Department of Civil and Environmental Engineering Michigan State UniversityEast Lansing MI 48824USA Water Resources Department Yangtze River Scientific Research InstituteWuhan 430010China

Streamflow and sediment transport in river ecosystems are crit-ical processes for material movement and energy conversion,intri-cately linked to socioeconomic and riverine biophysical processes such as hydropower development,flood mitigation,reservoir silta-tion,and channel adjustment[1].Streamflow-sediment load(Q-S)relationships are fundamental to improving the assessment and prediction of these riverine processes and flux *** has substantially regulated large river systems,leading to a signif-icant decreasing trend in both streamflow and sediment load,resulting in far-reaching impacts on water quality and ecological stability[2].In the context of intensifying hydrological extremes.

关键词： prediction Yangtze River Basin extreme streamflow hydropower developmentflood mitigationreservoir sediment relationships channel adjustment streamflow sediment dam regulation streamflow sediment transport

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Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization

arXiv

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arXiv 2023年

作者： Chen, Jinbiao Wang, Jiahai Zhang, Zizhen Cao, Zhiguang Ye, Te Chen, Siyuan School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training;meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Community-Aware Heterogeneous Graph Contrastive Learning 19th

Community-Aware Heterogeneous Graph Contrastive Learning

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Li, Xinying Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Recently, heterogeneous graph contrastive learning, which can mine supervision signals from the data, has attracted widespread attention. However, most existing methods employ random data augmentation strategies to construct contrastive views, which may destroy the semantic information in heterogeneous graphs. Moreover, they often select positive and negative samples based solely on node-level proximity and overlook hard samples that are difficult to distinguish from anchors. To solve the above problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning model called CAHGCL. In particular, we design an adaptive data augmentation strategy to construct views, including feature augmentation and topology augmentation. To improve the quality of samples, we propose a dynamic sample weighting strategy based on node similarity and community information, capable of identifying both hard positive samples and hard negative samples. Finally, we introduce community-level contrast to improve community cohesion. Extensive experiments and analyses demonstrate that CAHGCL consistently outperforms state-of-the-art baselines on three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Novel DDoS Detection Model for SDN Using Single-Class Cluster Oversampling and Weighted Ensemble Method

A Novel DDoS Detection Model for SDN Using Single-Class Clus...

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International Conference on Network Protocols

作者： Hao Zhang Shuqi Wu Jie Pan Zhi Wang Xiaolong Sun College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The centralized control plane characteristic of Software Defined Networking (SDN) makes it a prime target for Distributed Denial of Service (DDoS) attacks. A significant issue in network traffic data is the severe imbalance between background traffic and various types of DDoS attack traffic. To address this challenge, we propose a novel DDoS detection model based on single-class cluster oversampling and weighted ensemble method. Initially, we design a multi-strategy feature selection using variance inflation factor and recursive feature elimination. Next, we construct a single-class cluster model based on the spatial distribution of samples, and create minority class samples independently within each cluster. Finally, detection results are determined using the weight vector of each base classifier in ensemble learning. The proposed DDoS detection model's performance is validated through simulations of various DDoS attack scenarios in an SDN environment. Experimental results indicate that the proposed strategy exhibits superior performance, showing improvements in precision, recall, and F1 score compared to state-of-the-art techniques.

关键词： Interpolation Protocols Telecommunication traffic Denial-of-service attack Feature extraction Vectors Data models Ensemble learning Computer crime Software defined networking

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Research on Secure Communication Methods for High-Speed Cross-Network Data Exchange

Research on Secure Communication Methods for High-Speed Cros...

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Electronic Information engineering and Computer Communication (EIECC), International Conference on

作者： Yao Zhang Yitao Chen Jiujiang Fei Guojun Peng Rong Su Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China Wuhan Maritime Communication Research Institute Wuhan China Wuhan Railway Vocational College of Technology Wuhan China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

This paper introduces the application requirements for high-speed secure communication across networks, the fundamental principles of cross-network data exchange, and its implementation. Based on high-speed switch technology and proprietary protocols, a hardware-software co-design solution is proposed to achieve secure communication for cross-network data exchange. The solution eliminates potential security risks associated with unauthorized access, intrusion attacks, and information leakage in heterogeneous network interconnections. It establishes robust defense capabilities against illegal network intrusions, attacks, and failures, meeting the security communication requirements for controlled interconnection and cross-network data exchange between specialized networks.

关键词： Protocols Computer hacking Switches Information leakage Heterogeneous networks Network intrusion

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D-FGNAE: Decentralized Federated Graph Normalized AutoEncoder 19th

D-FGNAE: Decentralized Federated Graph Normalized AutoEncode...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Liang, Yuting Cai, Weixin Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Graphs widely exist in real-world, and Graph Neural Networks (GNNs) have exhibited exceptional efficacy in graph learning in diverse fields. With the strengthening of data privacy protection worldwide in recent years, Federated graph neural networks (FedGNNs) have gained increasing attention in academia and industry owing to their ability to train the model in a collaborative manner while complying with the privacy protection regulations. However, in federated learning, the non-independent and identically distributed (non-IID) problem of local data possessed by multiple participants can significantly undermine model accuracy. We propose a new Decentralized Federated Graph Normalized AutoEncoder (D-FGNAE). First, the model is designed as a decentralized federated learning framework with dynamically assigned tripartite roles. This design eliminates the fixed server role found in traditional federated learning, enhances system fault tolerance, avoids single points of failure, and protects model privacy. Second, the splitting and correcting of calculation by layer in the model, along with the special design of the normalization layer, effectively tackle the non-IID problem in both the structural and attribute aspects. Experimental results on real-world networks demonstrate the effectiveness of D-FGNAE, which can achieve nearly the same accuracy as the centralized model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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LCW: A Lightweight Recommendation Framework for Non-profit Crowdfunding Projects 21

LCW: A Lightweight Recommendation Framework for Non-profit C...

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4th International Conference on Computer Science and Software engineering, CSSE 2021

作者： Liu, Jinxin Xiao, Yingyuan Zheng, Wenguang Engineering Research Center of Learning-BasedIntelligent System Ministry of Education Tianjin University of Technology China Tianjin Key Laboratory OfIntelligence Computing and Novel Software Technology Tianjin University of Technology China

ISBN: (纸本)9781450390675

In recent years, crowdfunding platform has gradually emerged as a new economic model, and more and more people choose crowdfunding to obtain the initial funds needed by the project. However, with the increasing number of projects, it is difficult for sponsors to find suitable projects they want to invest in. Therefore, it is very important to build an efficient recommendation system on the crowdfunding platform. Different from previous crowdfunding recommendation systems designed to improve returns, this paper designs a lightweight recommendation framework LCW for non-profit crowdfunding platforms. A large number of experiments on real kiva data sets have proved the effectiveness of this method and it obtains the SOTA results. © 2021 ACM.

关键词： Recommender systems

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Community Evolution Tracking Based on High-Order Neighbor Consideration and Node Change Identification 19th

Community Evolution Tracking Based on High-Order Neighbor C...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Zhang, Yunan Wang, Chaohui Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Community evolution tracking is widely used in complex network analysis, which analyzes and identifies how communities evolve over time based on dynamic community detection. However, the current incremental dynamic community detection method has the phenomenon of ’concept drift’, which leads to inaccurate tracking of community evolution. Although some studies on dynamic community detection have proposed to perform global community detection on the network by timing or when the error information accumulates to a certain extent, this method will lead to a sudden change in community division, which will interrupt the evolution sequence of some communities and bring great difficulties to the continuous tracking of community evolution. In addition, the current community evolution tracking methods match the communities of two adjacent snapshots, and it is difficult to capture the fine-grained evolution process within the community. This paper proposes a community evolution tracking algorithm based on high-order neighbor consideration and node change identification. Firstly, based on an incremental update strategy that considers high-order neighbors, we adaptively expand the scope of incremental updates to reduce the accumulation of error information. Secondly, through the node change identification strategy to mine fine-grained community evolution events. Experiments show that our method can effectively track the evolution of communities in dynamic networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Community evolution Complex network Dynamic network Evolution tracking

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