标题： 动态特征融合：结合全局图结构和局部语义的区块链欺诈检测 显示英文标题

标题： Dynamic Feature Fusion: Combining Global Graph Structures and Local Semantics for Blockchain Fraud Detection

摘要： 区块链技术的出现促进了智能合约在金融领域的广泛应用。 然而，当前的欺诈检测方法在捕捉交易网络中的全局结构模式和交易数据中的局部语义关系方面存在局限性。 大多数现有模型仅关注结构信息或语义特征中的某一方面，导致在检测复杂欺诈模式时表现不佳。 在本文中，我们提出了一种动态特征融合模型，结合基于图的表示学习和语义特征提取用于区块链欺诈检测。 具体来说，我们构建全局图表示来建模账户关系，并从交易数据中提取局部上下文特征。 引入了一种动态多模态融合机制，以自适应地整合这些特征，使模型能够有效地捕捉结构和语义欺诈模式。 我们进一步开发了一个全面的数据处理流程，包括图构建、时间特征增强和文本预处理。 在大规模真实世界区块链数据集上的实验结果表明，我们的方法在准确率、F1分数和召回率指标上均优于现有基准。 这项工作突出了整合结构关系和语义相似性对于稳健欺诈检测的重要性，并为保障区块链系统提供了一个可扩展的解决方案。 显示英文摘要

摘要： The advent of blockchain technology has facilitated the widespread adoption of smart contracts in the financial sector. However, current fraud detection methodologies exhibit limitations in capturing both global structural patterns within transaction networks and local semantic relationships embedded in transaction data. Most existing models focus on either structural information or semantic features individually, leading to suboptimal performance in detecting complex fraud patterns.In this paper, we propose a dynamic feature fusion model that combines graph-based representation learning and semantic feature extraction for blockchain fraud detection. Specifically, we construct global graph representations to model account relationships and extract local contextual features from transaction data. A dynamic multimodal fusion mechanism is introduced to adaptively integrate these features, enabling the model to capture both structural and semantic fraud patterns effectively. We further develop a comprehensive data processing pipeline, including graph construction, temporal feature enhancement, and text preprocessing. Experimental results on large-scale real-world blockchain datasets demonstrate that our method outperforms existing benchmarks across accuracy, F1 score, and recall metrics. This work highlights the importance of integrating structural relationships and semantic similarities for robust fraud detection and offers a scalable solution for securing blockchain systems.