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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.
List of contents
.- 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.
