Current methods to prevent crypto asset fraud are based on the analysis of transaction graphs within blockchain networks. While effective for identifying transaction patterns indicative of fraud, it does not capture the semantics of transactions and is constrained to blockchain data. Consequently, preventive methods based on transaction graphs are inherently limited. In response to these limitations, we propose the Kosmosis approach, which aims to incrementally construct a knowledge graph as new blockchain and social media data become available. During construction, it aims to extract the semantics of transactions and connect blockchain addresses to their real-world entities by fusing blockchain and social media data in a knowledge graph. This enables novel preventive methods against rug pulls as a form of crypto asset fraud. To demonstrate the effectiveness and practical applicability of the Kosmosis approach, we examine a series of real-world rug pulls from 2021. Through this case, we illustrate how Kosmosis can aid in identifying and preventing such fraudulent activities by leveraging the insights from the constructed knowledge graph.

翻译：当前防范加密资产欺诈的方法主要基于对区块链网络内交易图的分析。尽管该方法能有效识别欺诈相关的交易模式，但无法捕捉交易语义，且局限于区块链数据。因此，基于交易图的防范方法存在固有局限性。针对这些不足，我们提出Kosmosis方法，旨在随着新区块链和社交媒体数据的出现，逐步构建知识图谱。在构建过程中，该方法通过融合区块链与社交媒体数据至知识图谱，致力于提取交易语义并将区块链地址与现实实体关联。这为防范作为加密资产欺诈形式之一的拉地毯骗局提供了创新的预防手段。为验证Kosmosis方法的有效性与实际适用性，我们分析了2021年发生的一系列真实拉地毯骗局案例。通过该案例，我们阐释了如何利用构建的知识图谱所获得的洞察，帮助识别和预防此类欺诈活动。