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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Abnormal Detection and Fault Diagnosis Method of Bearing Based on Deep Convolutional Generative Adversarial network

Abnormal Detection and Fault Diagnosis Method of Bearing Bas...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Yu Zhang Huijuan Hao Tixiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Unite Environmental Technology Co. Ltd Jinan China

The breakdowns of Rolling bearings have a large impact range and then cause high maintenance costs. To solve this problem, this paper studies a small-sample bearing anomaly detection and fault diagnosis method based on deep convolutional generative adversarial network. This method constructs a DC-GANomaly network model based on attention mechanism by fusing DCGAN and GANomaly networks and introducing attention mechanisms. In this paper, the bearing fault dataset of Western Reserve University is used to verify the proposed method, and the performance of the model is discussed in comparative experiments.

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Signature Scheme Based on The SM2 Algorithm in Fabric

Signature Scheme Based on The SM2 Algorithm in Fabric

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International Conference on networking and network Applications (NaNA)

作者： Yaling Zhang Hongyun Wang Yichuan Wang Wenjiang Ji Lei Zhu School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of technology Xi’an China

Based on the analysis and research of the widely used Hyperledger Fabric alliance chain structure, this paper proposes a digital signature scheme based on the national cryptographic algorithm SM2 which can be applied to Hyperledger Fabric. Firstly, two elliptic curve public-key cryptography algorithms are analyzed, and the feasibility of the SM2 in Fabric system is studied. Secondly, the Fabric system using the SM2 algorithm is designed and implemented, and the generation process of the Fabric chain address using the SM2 algorithm is given. Finally, experimental verification of the availability and performance of the Fabric system after the replacement of national cryptography algorithms is carried out. The experimental results show that the SM2 interface of the new Fabric alliance chain has good usability, and the average latency is reduced by 51.8%, and the transaction throughput is increased by 33.1%.

关键词： Distributed ledger Elliptic curves Public key cryptography Throughput Fabrics Blockchains Usability

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Research on the Design of Government Data Governance Evaluation System under the Perspective of Digital Government

Research on the Design of Government Data Governance Evaluat...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Zirui Xu Jinyang Sun Zhe Lu Delong Han Information Research Institute of Shandong Academy of Sciences Qilu University of Technology(Shandong Academy of Sciences) Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the context of the digital era, government data governance is a key factor in promoting the modernization of government governance, but there is a lack of a complete set of evaluation index system to standardize the content and scope of government data governance. This paper adopts the policy tool theory to code and analyze the 12 policies from the three policy tool dimensions of supply layer, demand layer, and environment layer, and proposes three first-level evaluation indicators of basic environment, actual effectiveness, and safeguard mechanism, on the basis of which the evaluation content is selected in a focused manner based on the integration of policy documents related to data governance. Meanwhile, a comprehensive, scientific and objective evaluation system of government data governance is constructed by adopting assessment means combining subjectivity and objectivity, and quantitative and qualitative. The system is designed to assess and guide the data governance practices of the domestic government, point out the direction for government departments at all levels to carry out data governance, and play an important role in promoting the transparency and efficiency of data management.

关键词： Codes Government Cyberspace Data science Data governance Indexes

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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Traffic Engineering in Dynamic Hybrid Segment Routing networks

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Computers, Materials & Continua 2021年第7期68卷 655-670页

作者： Yingya Guo Kai Huang Cheng Hu Jiangyuan Yao Siyu Zhou College of Mathematics and Computer Science Fuzhou UniversityFuzhou350000China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350000China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou350003China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyberspace Security Hainan UniversityHaiKou570228China Tandon School of Engineering New York UniversityNew York10012USA Department of Computing Hong Kong Polytechnic UniversityHong Hom999077Hong Kong

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet *** SR architecture,the paths that the packets choose to route on are indicated at the ingress *** with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress *** the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical *** partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable *** the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR *** algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering ***,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)***,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network *** the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in *** extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

关键词： Traffic engineering routing optimization segment routing deep reinforcement learning ultra-scalable spectral clustering

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IDFusion: An Infrared and Visible Image Fusion network for Illuminating Darkness

IDFusion: An Infrared and Visible Image Fusion Network for I...

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International Conference on Computer Supported Cooperative Work in Design

作者： Guohua Lv Xiyan Wang Zhonghe Wei Jinyong Cheng Guangxiao Ma Hanju Bao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Comuter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Lianxin Intelligent Technology (Shandong) Co. Ltd Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The purpose of infrared and visible image fusion is to combine the background information of the visible images and the thermal target information of the infrared images. Current fusion methods often neglect the challenges of low-light conditions. In the night scene, the existing methods fail to capture texture information from the visible images that is hidden in the darkness, producing suboptimal fusion results that can hinder subsequent visual applications. Therefore, we propose a method for infrared and visible image fusion under night scenes, termed as IDFusion. Specifically, our method is divided into three parts, first, a dense auto-encoder is designed to obtain more useful features from the source images. Then we design a brightness enhancement network that removes visible degraded illumination maps to obtain brightness-enhanced features. Finally, a texture fusion network is designed so that the infrared features and enhanced visible features avoid texture loss during the fusion process. Experimental results show that our network can obtain better fusion results, outperforming state-of-the-art methods in terms of subjective visual effects and quantitative metrics.

关键词： Measurement Federated learning Brightness Lighting Visual effects Image fusion

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Risk Modeling and Traceability of Boundary Attacks on Internet of Power Systems Based on Complex networks 9

Risk Modeling and Traceability of Boundary Attacks on Intern...

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

作者： Li, Xin Qu, Zhaoyang Yu, Tong Xie, Ming Yu, Fu Ding, Wei Northeast Electric Power University School of Electrical Engineering Jilin132012 China People's Bank of China Qinghai Provincial Branch Xining810000 China China Southern Power Grid Guangxi Power Grid Co. Ltd. Research Institute Nanning530023 China China Guizhou Power Grid Co. Ltd. Bijie Power Supply Bureau Bijie551700 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 Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350391367

With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines. © 2024 IEEE.

关键词： Internet of things

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Cross Aggregation network for Semantic Segmentation

SSRN

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SSRN 2022年

作者： Zhao, Minghua Zhi, Yuxing Du, Shuangli Hei, Xinhong Hu, Jing Shi, Cheng Li, Peng School of Computer Science and Engineering Xi’an University of Technology No.5 South Jinhua Road Shaanxi Xi’an710048 China Shaanxi Key Laboratory of Network Computing and Security Technology No.5 South Jinhua Road Shaanxi Xi’an710048 China

To achieve more accurate prediction, advanced semantic segmentation methods are explored in the way of context modeling. Images in real scenes usually contain multi-scale objects and contents. The feature propagation form in convolution networks is very important to capture multi-scale context and obtain accurate segmentation. This paper proposes a novel pattern of information flow aggregation to rich features expression, called Cross Aggregation Module (CRA), In CRA, the flows of different scales are transmitted to the context aggregation module (CAM) through a parallel-cross connection to generate a feature map containing more information to aggregate multi-scale features and capture remote context information. We further add the low-level features in the later stage in the encoder to enhance the features interaction. Based on these developments, we build a Context Cross Aggregation network (CRANet) which employs an asymmetric decoder to restore the scale of the predicted feature map. The proposed CRANet is evaluated on two challenging datasets, i.e., Cityscapes and Camvid. The experiments show that the proposed network achieves competitive performance. © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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Data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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