A Taxonomy of Anti-Fraud Measures within Token Economy: Insights from Rug Pull Schemes

Decentralized Finance is still a growing sector that locks in billions of USD and serves as a platform for scammers to defraud investors. The most common fraud is the so-called rug pull, which has been researched for years. This paper presents the proposed approaches to combat this scheme and proposes a taxonomy to categorize solutions according to their best application. Solutions can be applied at three different levels. The first is at the service level, where the data is freely available on the blockchain. The second is at the user level, by deploying smart contracts that consist of preventive measures; and lastly, at the verifier level, which can prevent malicious transactions in the same way they prevent double-spending. This paper contributes in three ways. It gives an overview of all published approaches to predict and prevent malicious transactions. It also presents a taxonomy to facilitate development for specific use cases. Finally, it reveals a gap in the current research landscape.

Authors

Institutions

Publication Details

Published
2024-10-09
DOI
https://doi.org/10.1109/brains63024.2024.10732222
Citations
2
Primary Topic
Taxation and Compliance Studies
Type
article
Field-Weighted Citation Impact
2.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Taxonomy of Anti-Fraud Measures within Token Economy: Insights from Rug Pull Schemes

Philip Raschke, Christian René Sechting
2 citations
Taxation and Compliance Studies
2.26
article

A Taxonomy of Anti-Fraud Measures within Token Economy: Insights from Rug Pull Schemes

Philip Raschke, Christian René Sechting
article en
2 citations

Abstract

Decentralized Finance is still a growing sector that locks in billions of USD and serves as a platform for scammers to defraud investors. The most common fraud is the so-called rug pull, which has been researched for years. This paper presents the proposed approaches to combat this scheme and proposes a taxonomy to categorize solutions according to their best application. Solutions can be applied at three different levels. The first is at the service level, where the data is freely available on the blockchain. The second is at the user level, by deploying smart contracts that consist of preventive measures; and lastly, at the verifier level, which can prevent malicious transactions in the same way they prevent double-spending. This paper contributes in three ways. It gives an overview of all published approaches to predict and prevent malicious transactions. It also presents a taxonomy to facilitate development for specific use cases. Finally, it reveals a gap in the current research landscape.

Technische Universität Berlin (DE)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Taxation and Compliance Studies
2.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.