发表机构
tsai-unb（tsai - 新不伦瑞克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对元宇宙平台带来的欺诈等新挑战及现有数据集局限性，提出TSAI-MetaFraud基准数据集，整合多类信息与欺诈场景，定义多项基准任务并进行基线评估，为元宇宙生态系统相关研究提供基准。
AI 中文摘要
元宇宙平台的出现创造了虚拟经济，带来了与欺诈、机器人活动和非法金融行为相关的新挑战。尽管对可信的元宇宙分析兴趣日增，但现有数据集通常孤立地关注用户行为、认证或金融交易，限制了多模态欺诈检测方法的发展和可重复评估。为填补这一空白，我们提出TSAI-MetaFraud，一个用于虚拟经济中欺诈分析的多模态、多任务基准数据集。它整合了行为、交易和图结构信息，纳入了现实的欺诈和自动化机器人场景。我们定义了基准任务，包括交易欺诈检测、跨模态节点分类、时间链接预测和弱监督欺诈检测，并使用机器学习模型和图神经网络进行了基线评估。通过在统一的虚拟经济中联合捕捉行为活动、金融互动和关系结构，TSAI-MetaFraud为新兴元宇宙生态系统中的多模态学习、图挖掘、欺诈分析和可信人工智能提供了一个基准。
英文摘要
The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing interest in trustworthy metaverse analytics, existing datasets typically focus on user behavior, authentication, or financial transactions in isolation, limiting the development and reproducible evaluation of multimodal fraud detection methods. To address this gap, we present TSAI-MetaFraud, a multimodal, multi-task benchmark dataset for fraud analytics in virtual economies. TSAI-MetaFraud integrates behavioral, transactional, and graph-structured information while incorporating realistic fraud and automated bot scenarios. We define benchmark tasks including transaction fraud detection, cross-modal node classification, temporal link prediction, and weakly supervised fraud detection, and provide baseline evaluations using machine learning models and graph neural networks. By jointly capturing behavioral activity, financial interactions, and relational structure within a unified virtual economy, TSAI-MetaFraud provides a benchmark for advancing multimodal learning, graph mining, fraud analytics, and trustworthy AI in emerging metaverse ecosystems.
