The Kosmosis Approach to Crypto Rug Pull Detection
For blockchain security researchers and practitioners, this addresses the limitation of existing transaction-graph-based fraud detection methods by incorporating semantic and external data.
The paper proposes Kosmosis, a knowledge graph approach that fuses blockchain and social media data to detect crypto rug pulls by capturing transaction semantics and linking addresses to real-world entities, demonstrated through real-world case studies.
Crypto rug pulls have become a major threat to the integrity of blockchain ecosystems, with illicit activities surging and resulting in significant financial losses. Existing approaches to detect crypto asset fraud are based on the analysis of transaction graphs within blockchain networks. While effective for identifying transaction patterns indicative of fraud, existing approaches do not capture the semantics of transactions and are 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 connects 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. Through this case, we illustrate how Kosmosis can aid in identifying such fraudulent activities by leveraging the insights from the constructed knowledge graph.