Code will tell: Visual identification of Ponzi schemes on Ethereum

Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help inves...

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Main Authors: WEN, Xiaolin, YEO, Kim Siang, WANG, Yong, CHENG, Ling, ZHU, Feida, ZHU, Min
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/8577
https://ink.library.smu.edu.sg/context/sis_research/article/9580/viewcontent/3544549.3585861.pdf
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spelling sg-smu-ink.sis_research-95802024-08-01T00:33:17Z Code will tell: Visual identification of Ponzi schemes on Ethereum WEN, Xiaolin YEO, Kim Siang WANG, Yong CHENG, Ling ZHU, Feida ZHU, Min Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help investors easily identify Ponzi schemes and validate whether a smart contract is actually a Ponzi scheme. To fill the research gap, we propose PonziLens, a novel visualization approach to help investors achieve early identification of Ponzi schemes by investigating the operation codes of smart contracts. Specifically, we conduct symbolic execution of opcode and extract the control flow for investing and rewarding with critical opcode instructions. Then, an intuitive directed-graph based visualization is proposed to display the investing and rewarding flows and the crucial execution paths, enabling easy identification of Ponzi schemes on Ethereum. Two usage scenarios involving both Ponzi and non-Ponzi schemes demonstrate the effectiveness of PonziLens. 2023-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8577 info:doi/10.1145/3544549.3585861 https://ink.library.smu.edu.sg/context/sis_research/article/9580/viewcontent/3544549.3585861.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Blockchain crypto-currency on-chain data analysis Ethereum Databases and Information Systems Finance and Financial Management Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Blockchain
crypto-currency
on-chain data analysis
Ethereum
Databases and Information Systems
Finance and Financial Management
Software Engineering
spellingShingle Blockchain
crypto-currency
on-chain data analysis
Ethereum
Databases and Information Systems
Finance and Financial Management
Software Engineering
WEN, Xiaolin
YEO, Kim Siang
WANG, Yong
CHENG, Ling
ZHU, Feida
ZHU, Min
Code will tell: Visual identification of Ponzi schemes on Ethereum
description Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help investors easily identify Ponzi schemes and validate whether a smart contract is actually a Ponzi scheme. To fill the research gap, we propose PonziLens, a novel visualization approach to help investors achieve early identification of Ponzi schemes by investigating the operation codes of smart contracts. Specifically, we conduct symbolic execution of opcode and extract the control flow for investing and rewarding with critical opcode instructions. Then, an intuitive directed-graph based visualization is proposed to display the investing and rewarding flows and the crucial execution paths, enabling easy identification of Ponzi schemes on Ethereum. Two usage scenarios involving both Ponzi and non-Ponzi schemes demonstrate the effectiveness of PonziLens.
format text
author WEN, Xiaolin
YEO, Kim Siang
WANG, Yong
CHENG, Ling
ZHU, Feida
ZHU, Min
author_facet WEN, Xiaolin
YEO, Kim Siang
WANG, Yong
CHENG, Ling
ZHU, Feida
ZHU, Min
author_sort WEN, Xiaolin
title Code will tell: Visual identification of Ponzi schemes on Ethereum
title_short Code will tell: Visual identification of Ponzi schemes on Ethereum
title_full Code will tell: Visual identification of Ponzi schemes on Ethereum
title_fullStr Code will tell: Visual identification of Ponzi schemes on Ethereum
title_full_unstemmed Code will tell: Visual identification of Ponzi schemes on Ethereum
title_sort code will tell: visual identification of ponzi schemes on ethereum
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/8577
https://ink.library.smu.edu.sg/context/sis_research/article/9580/viewcontent/3544549.3585861.pdf
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