Blockchain, Metaverse and Trustworthy Systems

6th International Conference, BlockSys 2024, Hangzhou, China, July 12-14, 2024, Revised Selected Papers, Part I
Erscheinungsjahr 2025
ISBN: 978-981-961410-3
Verlag: Springer Nature Singapore

Buch, Englisch, Band 2264, 246 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 400 g

Reihe: Communications in Computer and Information Science

This two-volume set CCIS 2264 and CCIS 2265 constitutes the refereed proceedings of the 6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024, held in Hangzhou, China, during July 12–14, 2024.

The 34 full papers  presented in these two volumes were carefully reviewed and selected from 74 submissions. The papers are organized in the following topical sections:

Part I: Blockchain and Data Mining; Data Security and Anomaly Detection; Blockchain Performance Optimization.

Part II: Frontier Technology Integration; Trustworthy System and Cryptocurrencies; Blockchain Applications.

Zielgruppe

Research

Autoren/Hrsg.

He, Debiao

Huang, Huawei

Wu, Jiajing

Wang, Chen

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Betriebssysteme
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie

Inhaltsverzeichnis

.- Blockchain and Data Mining.

.- Intrusion Anomaly Detection with Multi-Transformer.

.- A Federated Learning Method Based on Linear Probing and Fine-Tuning.

.- Facilitating Feature and Topology Lightweighting: An Ethereum Transaction Graph Compression Method for Malicious Account Detection.

.- A Secure Hierarchical Federated Learning Framework based on FISCO Group Mechanism.

.- Research on Network Traffic Anomaly Detection Method Based on Deep Learning.

.- Hyper-parameter Optimization and Proxy Re-encryption for Federated Learning.

.- Data Security and Anomaly Detection.

.- Exploring Embedded Content in the Ethereum Blockchain: Data Restoration and Analysis.

.- Task Allocation and Process Optimization of Data, Information, Knowledge, and Wisdom (DIKW)-based Workflow Engine.

.- Location Data Sharing Method Based on Blockchain and Attribute-Based Encryption.

.- Implicit White-Box Implementations of Efficient Double-Block-Length MAC.

.- A Survey on Blockchain Scalability.

.- Supply Chain Financing Model Embedded with “Full-Process” Blockchain.

.- Blockchain Performance Optimization.

.- ReCon: Faster Smart Contract Vulnerability Detection by Reusable Symbolic Execution Tree.

.- SVD-SESDG: Smart Contract Vulnerability  Detection Technology via Symbol Execution and  State Variable Dependency Graph.

.- Dual-view Aware Smart Contract Vulnerability Detection for Ethereum.

.- Blockchain Layered Sharding Algorithm Based on Transaction Characteristics.

.- An Empirical Study on the Performance of EVMs and Wasm VMs for Smart Contract Execution.

.- Ponzi Scheme Detection in Smart Contracts Using Heterogeneous Semantic Graph.