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Research on distributed relational database based on MySQL

Research on distributed relational database based on MySQL

引用

2023 international Conference on computer, Artificial Intelligence, and Control Engineering, CAICE 2023

作者： Shi, Lanying Wang, Kai Li, Man Tong, Jiangang Qiao, Hongming Jiang, Yiquan China Research Institute of China Telecom Co. Ltd. Guangzhou510000 China China Mobile Information Technology Co. Ltd. Guangzhou510000 China China Research Institute of China Telecom Co. Ltd. Shanghai200000 China

ISBN: (纸本)9781510665040

With the rapid development of big data, cloud computing, artificial intelligence and other technologies, the expansion of database application requirements and the change of computer hardware environment, especially the rapid development of computer network and digital communication technology, satellite communications, cellular communications, computer local area networks, wide area networks, and the proliferation of Intranet and Internet have been widely used, resulting in the emergence of distributed database systems. Especially for basic industries such as telecommunications, databases often carry thousands of business systems, and millions of queries and tens of millions of bills are carried out every day. In the face of such massive scale, high concurrency, data security, realtime analysis and other multi scenario problems, the processing capacity of traditional centralized databases is far from meeting the needs. For this reason, this paper proposes a distributed database based on MySQL transformation. Through research and implementation, it can solve the above problems, meet the requirements of distributed transaction processing, ensure data backup, high availability and high performance, and improve efficiency. This paper will also describe its modeling model and sharding algorithm in detail. © 2023 SPIE.

关键词： Wide area networks

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Modern smart distribution network coordination planning technology and examples of distributed power source access planning 9

Modern smart distribution network coordination planning tech...

引用

9th international symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Zhao, Zihao Zhao, Ziheng Liu, Yanru Guo, Yue Bai, Yu Distribution Network Planning Department of State Grid HEBEI Economic Research Institute Co. Ltd Hebei Province Shijiazhuang050000 China Distribution Network Planning Department of State Grid Economic and Technological Research Institute Co. Ltd Beijing102200 China

ISBN: (纸本)9781510683303

Due to the introduction of a large number of distributed resources and other new elements, the distribution network presents new characteristics that are different from traditional power grids, thus posing new requirements for the planning work of the distribution network. The future distribution network planning needs to meet the requirements of primary and secondary collaborative coordination planning. Collaborative coordination is reflected in the formulation of planning schemes, that is, while carrying out the primary network planning, it is necessary to simultaneously consider the configuration of relay protection, safety automatic devices, as well as distribution automation and communication configuration, so as to coordinate the primary and secondary schemes. This article mainly introduces the main steps of modern smart distribution network planning technology, and provides an example application of collaborative planning for distributed power generation access. The adoption of primary and secondary coordinated planning helps to achieve the development goals of modern distribution networks that are safe, reliable, cost-effective, clean, and low-carbon. © 2024 SPIE.

关键词： Power distribution planning

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Formally verified asymptotic consensus in robust networks 30th

Formally verified asymptotic consensus in robust networks

引用

30th international Conference on Tools and Algorithms for the Construction and Analysis of systems (TACAS) Held as Part of the 27th European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Tekriwal, Mohit Tachna-Fram, Avi Jeannin, Jean-Baptiste Kapritsos, Manos Panagou, Dimitra Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9783031572456;9783031572463

distributed architectures are used to improve performance and reliability of various systems. Examples include drone swarms and load-balancing servers. An important capability of a distributed architecture is the ability to reach consensus among all its nodes. Several consensus algorithms have been proposed, and many of these algorithms come with intricate proofs of correctness, that are not mechanically checked. In the controls community, algorithms often achieve consensus asymptotically, e.g., for problems such as the design of human control systems, or the analysis of natural systems like bird flocking. This is in contrast to exact consensus algorithm such as Paxos, which have received much more recent attention in the formal methods community. This paper presents the first formal proof of an asymptotic consensus algorithm, and addresses various challenges in its formalization. Using the Coq proof assistant, we verify the correctness of a widely used consensus algorithm in the distributed controls community, the Weighted-Mean Subsequence Reduced (W-MSR) algorithm. We formalize the necessary and sufficient conditions required to achieve resilient asymptotic consensus under the assumed attacker model. During the formalization, we clarify several imprecisions in the paper proof, including an imprecision on quantifiers in the main theorem.

关键词： Resilient asymptotic consensus W MSR algorithm Network robustness

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Brief Announcement: Understanding Self-stabilizing Node-Capacitated Overlay networks Through Simulation 1

引用

25th international symposium on Stabilization, Safety, and Security of distributed systems, SSS 2023

作者： Afeaneku, Winfred Berns, Andrew Kuchenberg, Weston Leisinger, Sara Liu, Cedric Department of Computer Science University of Northern Iowa Cedar FallsIA United States Cedar Falls High School Cedar FallsIA United States

ISBN: (数字)9783031442742

ISBN: (纸本)9783031442735

Overlay networks, where connections are made over logical links composed of zero or more physical links, are a popular paradigm in modern distributed computing. The use of logical links allows the creation of a variety of network topologies with desirable properties such as low degree and low diameter, regardless of the (usually) fixed physical topology. Many of these overlay networks operate in unfriendly environments where transient faults are commonplace. Self-stabilizing overlay networks present one way to manage these faults. In particular, self-stabilizing overlay networks can guarantee that the desired network topology is created when starting from any weakly-connected initial state. To date, work on self-stabilizing overlay networks has assumed the network has either unbounded bandwidth, or that the bandwidth constraints are placed on the communication links themselves. In practice, however, the bandwidth constraints are actually capacities on the nodes: adding and deleting logical links does not change the fixed physical links being used. In this work, we describe the node-capacitated model for self-stabilizing overlay networks. To better understand this new model, we created a simulation and ran it numerous times while adjusting various parameters. We discuss this simulation and several experiments. Finally, we propose future directions for self-stabilizing node-capacitated overlay networks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Overlay networks

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Topology identification of distribution network based on feature selection and convolutional neural network 9

Topology identification of distribution network based on fea...

引用

9th international symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Zhao, Zihui Cheng, Shun Wu, Weiyi Gao, Xueying Zhang, Huimin Zhou, Xinghua Li, Panni Sun, Jie Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangdong Zhuhai519075 China Beijing Join Bright Digital Power Technology Co. Ltd. Beijing100085 China

ISBN: (纸本)9781510683303

In the context of smart grid and new power system, distributed generations, energy storage systems, electric vehicles and other new power equipment are connected to the distribution network on a large scale, which makes the topology of the distribution network change more frequently. The traditional topology identification methods of the distribution network are limited by measuring equipment, and difficult to apply. Aiming at the difficulty of obtaining real-time topology of distribution network, this paper proposes a topology identification based on feature selection and Convolutional Neural Network. Firstly, XGBoost is used to calculate the feature importance of node voltage amplitude, and then modified according to the correlation analysis results to select topology identification features. This step can be used to reduce the time and space complexity of the model. Secondly, a distribution network topology identification model based on Convolutional Neural Network is constructed. The correlation between the feature data and the topology is learned by using the operation characteristics of convolutional and pooling and the characteristics of the model structure. Then the mapping rules are established. Finally, IEEE33-node distribution network is used to verify the effectiveness of the proposed method in feature selection and topology identification. © 2024 SPIE.

关键词： Power distribution networks

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DNS Covert Channel Attack Detection Based on Spatio-Temporal Feature Fusion 22

DNS Covert Channel Attack Detection Based on Spatio-Temporal...

引用

22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： You, Wei Zhao, Chunlei Shan, Chao Dong, Guozhong Yu, Jie Guan, Yong Tianjin University of Technology Tianjin Key Laboratory of Intelligent Computing and Software New Technology Tianjin China Coordination Center of China Hubei Branch of National Computer Network Emergency Response Technical Team HuBei China Pengcheng Laboratory Department of New Networks Shenzhen China

ISBN: (纸本)9798331509712

An increasing number of Advanced Persistent Threat (APT) organizations are utilizing DNS (Domain Name System) covert channels to evade network intrusion detection systems and establish private authoritative servers for remote attacks. However, current detection models for DNS covert channels often have poor performance and limited generalization ability. To address these challenges, we propose a DNS covert channel attack detection method based on spatiotemporal feature fusion. This method combines the Interaction timing sequence features of DNS traffic with the spatial statistical features of domain names, and constructs a detection model called ConvSLSTM, which integrates a Convolutional Neural Network (CNN) with a stacked Long Short Term Memory (LSTM) network. CNN extracts local spatial features from traffic, while stacked LSTM captures Interaction timing sequence features, significantly improving detection accuracy. This article uses three typical public datasets for experiments, which contain a total of 440000 DNS covert channel attack data. Comparative experiments show that our method is significantly superior to existing methods and exhibits stronger generalization ability in detecting DNS covert channel attacks constructed by unknown tools. © 2024 IEEE.

关键词： Network intrusion

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Safeguarding E Commerce and Preventing Financial Fraud with AdaBoost CNN Cybersecurity Approach

Safeguarding E Commerce and Preventing Financial Fraud with ...

引用

2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Malathi, V. Shammi, L. Unni, Manu Vasudevan Babulal Nadaf, Akabarsaheb Devi, S. Rukmani Thangam, E. Dr. N.G.P Arts and Science College Department of Computer Science with Cyber Security Coimbatore India East Point College of Engineering and Technology Deptartment of Computer Science Bengaluru India St. Claret College Autonomous Department of Management Bangalore India Abhijit Kadam Institute of Management Ame Social Sciences Solapur India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Chennai India Ramco Institute of Technology Department of Eee Rajapalayam India

ISBN: (纸本)9798350375442

Examining the cyber security risks faced by companies and e-commerce platforms is the focus of this research. People in the corporate world and those in academia are curious about the technology applications of e-commerce. There are now possibilities that were before unimaginable for both businesses and consumers. On the other hand, there were certain issues with its arrival, chief among them being cyber security. Among the many forms of cybercrime, this study focuses on social engineering, denial-of-service attacks, malware, and data breaches. This proposed approach consists of three phases, which are feature extraction, model training, and data preprocessing. Addressing missing values, normalizing features, and eliminating noise are all part of preprocessing, which aims to clean and organize raw data. When dealing with multivariate data, principal component analysis (PCA) is a common tool for feature extraction. Throughout the training process, we employed an AdaBoost-CNN. With an average accuracy of 90.65%, this innovative technique surpasses CNN and AdaBoost © 2024 IEEE.

关键词： Cyber attacks

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A Multi-agent Model for Opinion Evolution in Social networks Under Cognitive Biases 44th

A Multi-agent Model for Opinion Evolution in Social Networks...

引用

44th international Conference on Formal Techniques for distributed Objects, Components and systems (FORTE)

作者： Alvimi, Mario S. da Silva, Artur Gaspar Knight, Sophia Valencia, Frank Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil Univ Minnesota Duluth Dept Comp Sci Duluth MN USA Ecole Polytech Paris CNRS LIX Palaiseau France Pontificia Univ Javeriana Cali Cali Colombia

ISBN: (纸本)9783031626449;9783031626456

We generalize the DeGroot model for opinion dynamics to better capture realistic social scenarios. We introduce a model where each agent has their own individual cognitive biases. Society is represented as a directed graph whose edges indicate how much agents influence one another. Biases are represented as the functions in the square region [-1, 1](2) and categorized into four sub-regions based on the potential reactions they may elicit in an agent during instances of opinion disagreement. Under the assumption that each bias of every agent is a continuous function within the region of receptive but resistant reactions (R), we show that the society converges to a consensus if the graph is strongly connected. Under the same assumption, we also establish that the entire society converges to a unanimous opinion if and only if the source components of the graph-namely, strongly connected components with no external influence-converge to that opinion. We illustrate that convergence is not guaranteed for strongly connected graphs when biases are either discontinuous functions in R or not included in R. We showcase our model through a series of examples and simulations, offering insights into how opinions form in social networks under cognitive biases.

关键词： Cognitive bias Multi-Agent systems Social networks

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Diverse Recommendations With Maximum Entropy Neighbor Selection and Graph Contrast Learning 22

Diverse Recommendations With Maximum Entropy Neighbor Select...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Zhou, Jinhao Chen, Pinghua Chen, Yunhua Zhou, Honghong Guangdong University of Technology School of Computer Science and Technology Guangzhou China Innovation Monitoring and Research Center Guangdong Science & Technology Guangzhou China

ISBN: (纸本)9798331509712

It has become a great challenge to balance accuracy and diversity in recommendation systems. Graph Neural networks (GNNs), while powerful, can lead to node representation homogeneity and information redundancy due to the indiscriminate aggregation of neighbor information. Therefore we propose a novel method that integrates maximum entropy neighbor selection with graph contrast learning to enhance the diversity of recommendations. The method introduces a strategy for neighbor selection based on maximum entropy to ensure a diverse subset of neighbors is chosen during the aggregation phase. A layer attention mechanism is implemented to address the over-smoothing issue, directing greater focus on higher-order neighbors. Furthermore, a loss re-weighting technique is applied to emphasize the learning of long-tail items. The overarching objective is to significantly improve recommendation diversity while maintaining system accuracy, underpinned by graph contrast learning method. Experimental results on the Beauty and MIND-small datasets demonstrate significant enhancements in the accuracy and diversity metrics of the proposed method. In particular, regarding the Recall@300 metric, a substantial improvement of up to 30.56% is observed. Conversely, the method experiences a mere 4.63% reduction in accuracy compared to the optimal baseline. This indicates that the proposed method markedly amplifies the diversity of recommendations without significantly compromising recommendation accuracy. © 2024 IEEE.

关键词： Contrastive Learning

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VANETGuard: A Secure Hybrid Trust Management System for VANETs 16

VANETGuard: A Secure Hybrid Trust Management System for VANE...

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16th international Conference on Ambient systems, networks and Technologies networks, ANT 2025 / 8th international Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Almaziad, Reem Kurdi, Heba Department of Computer Science King Saud University Riyadh Saudi Arabia

This study introduces a secure and scalable hybrid trust management system for Vehicular Ad Hoc networks (VANETs), designed to detect and mitigate diverse malicious activities, including Sybil attacks, Denial of Service (DoS), and data forgery. Integrating entropy-based anomaly detection, Bayesian reputation updating, and the IOTA Tangle as a distributed ledger, the system ensures robust, real-time trust evaluation in dynamic vehicular environments. Rigorous testing against adversarial scenarios demonstrated high detection accuracy, low latency, and resilience under varied attack conditions. Key contributions include enhanced scalability through edge computing, secure reputation storage using distributed Ledger Technology (DLT), and real-time anomaly detection. © 2025 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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