The Kosmosis Use-Case of Crypto Rug Pull Detection and Prevention
Philipp Stangl, Christoph P. Neumann

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…
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Cryptographic Implementations and Security · Advanced Data Storage Technologies
