The Kosmosis Use-Case of Crypto Rug Pull Detection and Prevention

TL;DR

The paper introduces Kosmosis, a knowledge graph-based approach that combines blockchain and social media data to better detect and prevent crypto rug pull frauds by capturing transaction semantics.

Contribution

It presents a novel incremental knowledge graph construction method that fuses blockchain and social media data for improved crypto fraud prevention.

Findings

Effective in identifying real-world rug pulls from 2021

Enhances fraud detection by capturing transaction semantics

Demonstrates practical applicability in crypto fraud prevention

Abstract

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.