Publication Type

Journal Article

Version

acceptedVersion

Publication Date

9-2025

Abstract

With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still missing. To fill the research gap, we first extract semantic-meaningful actions to represent the execution behaviors specified in smart contract bytecodes, which are derived from a literature review and in-depth interviews with domain experts. We then propose PonziLens+, a novel visual analytic approach that provides an intuitive and reliable analysis of Ponzi-scheme-related features within these execution behaviors. PonziLens+ has three visualization modules that intuitively reveal all potential behaviors of a smart contract, highlighting fraudulent features across three levels of detail. It can help smart contract investors and auditors achieve confident identification of any smart Ponzi schemes. We conducted two case studies and in-depth user interviews with 12 domain experts and common investors to evaluate PonziLens+. The results demonstrate the effectiveness and usability of PonziLens+ in achieving an effective identification of smart Ponzi schemes

Keywords

Smart Ponzi Scheme, Visual Analytics, Blockchain, Smart Contracts

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering; Cybersecurity; Software and Cyber-Physical Systems

Publication

IEEE Transactions on Visualization and Computer Graphics

Volume

31

Issue

9

First Page

6451

Last Page

6465

ISSN

1077-2626

Identifier

10.1109/TVCG.2024.3516379

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TVCG.2024.3516379

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