research-article

Tracking Counterfeit Cryptocurrency End-to-end

Authors:

Gareth TysonAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 4, Issue 3

Article No.: 50, Pages 1 - 28

https://doi.org/10.1145/3428335

Published: 30 November 2020 Publication History

Abstract

The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that counterfeit cryptocurrency has also appeared. In this paper, we empirically explore the presence of counterfeit cryptocurrencies on Ethereum and measure their impact. By analyzing over 190K ERC-20 tokens (or cryptocurrencies) on Ethereum, we have identified $2,117$ counterfeit tokens that target 94 of the 100 most popular cryptocurrencies. We perform an end-to-end characterization of the counterfeit token ecosystem, including their popularity, creators and holders, fraudulent behaviors and advertising channels. Through this, we have identified two types of scams related to counterfeit tokens and devised techniques to identify such scams. We observe that over 7,104 victims were deceived in these scams, and the overall financial loss sums to a minimum of \$ 17 million (74,271.7 ETH). Our findings demonstrate the urgency to identify counterfeit cryptocurrencies and mitigate this threat.

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Li KGuan SLee DGaretto MMarin ACiucu FFanti GRighter R(2024)Towards Understanding and Characterizing the Arbitrage Bot Scam In the WildAbstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems10.1145/3652963.3655088(89-90)Online publication date: 10-Jun-2024
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Index Terms

Tracking Counterfeit Cryptocurrency End-to-end
1. Information systems
  1. World Wide Web
    1. Web mining
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
  2. Software and application security
    1. Web application security

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Published In

cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 4, Issue 3

POMACS

December 2020

345 pages

EISSN:2476-1249

DOI:10.1145/3440131

Editors:
Augustin Chaintreau
Columbia University
,
Leana Golubchik
University of Southern California
,
Zhi-Li Zhang
University of Minnesota

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Publication History

Published: 30 November 2020

Published in POMACS Volume 4, Issue 3

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Cited By

Li KGuan SLee D(2024)Towards Understanding and Characterizing the Arbitrage Bot Scam In the WildACM SIGMETRICS Performance Evaluation Review10.1145/3673660.365508852:1(89-90)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3673660.3655088
Li KGuan SLee DGaretto MMarin ACiucu FFanti GRighter R(2024)Towards Understanding and Characterizing the Arbitrage Bot Scam In the WildAbstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems10.1145/3652963.3655088(89-90)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3652963.3655088
Lin DWu JYu YFu QZheng ZYang CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)DenseFlow: Spotting Cryptocurrency Money Laundering in Ethereum Transaction GraphsProceedings of the ACM Web Conference 202410.1145/3589334.3645692(4429-4438)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645692
Ye GHong GZhang YYang MChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Interface Illusions: Uncovering the Rise of Visual Scams in Cryptocurrency WalletsProceedings of the ACM Web Conference 202410.1145/3589334.3645348(1585-1595)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645348
Lin DWu JHuang TLin KZheng Z(2024)Who is Who on Ethereum? Account Labeling Using Heterophilic Graph Convolutional NetworkIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.332952054:3(1541-1553)Online publication date: Mar-2024
https://doi.org/10.1109/TSMC.2023.3329520
Qi YWu JXu HGuizani M(2024)Blockchain Data Mining With Graph Learning: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.332740446:2(729-748)Online publication date: Feb-2024
https://doi.org/10.1109/TPAMI.2023.3327404
Wu JLin DFu QYang SChen TZheng ZSong B(2024)Toward Understanding Asset Flows in Crypto Money Laundering Through the Lenses of Ethereum HeistsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.334627619(1994-2009)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3346276
Abulaish MDalal H(2024)The Dark Crypto World!IEEE Internet Computing10.1109/MIC.2024.337370628:3(26-36)Online publication date: 7-Mar-2024
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Cernera FLa Morgia MMei ASassi FCalandrino JTroncoso C(2023)Token spammers, rug pulls, and sniper botsProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620425(3349-3366)Online publication date: 9-Aug-2023
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Kashyap R(2023)DeFi Security: Turning The Weakest Link Into The Strongest AttractionSSRN Electronic Journal10.2139/ssrn.4528793Online publication date: 2023
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