TY - GEN
T1 - Trade or Trick? Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange
AU - Xia, Pengcheng
AU - Wang, Haoyu
AU - Gao, Bingyu
AU - Su, Weihang
AU - Yu, Zhou
AU - Luo, Xiapu
AU - Zhang, Chao
AU - Xiao, Xusheng
AU - Xu, Guoai
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/6/6
Y1 - 2022/6/6
N2 - The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Uniswap, as the most prominent cryptocurrency decentralized exchange (DEX), is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first investigate the landscape of cryptocurrency trading on Uniswap from different perspectives based on its transactions. Then, we propose an accurate approach for flagging scam tokens on Uniswap. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens are created specialized for the "rug pull"scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem.
AB - The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Uniswap, as the most prominent cryptocurrency decentralized exchange (DEX), is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first investigate the landscape of cryptocurrency trading on Uniswap from different perspectives based on its transactions. Then, we propose an accurate approach for flagging scam tokens on Uniswap. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens are created specialized for the "rug pull"scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem.
KW - blockchain
KW - exchange
KW - scam cryptocurrency
KW - uniswap
UR - http://www.scopus.com/inward/record.url?scp=85132183811&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85132183811&partnerID=8YFLogxK
U2 - 10.1145/3489048.3522636
DO - 10.1145/3489048.3522636
M3 - Conference contribution
AN - SCOPUS:85132183811
T3 - SIGMETRICS/PERFORMANCE 2022 - Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
SP - 23
EP - 24
BT - SIGMETRICS/PERFORMANCE 2022 - Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
PB - Association for Computing Machinery, Inc
T2 - 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/PERFORMANCE 2022
Y2 - 6 June 2022 through 10 June 2022
ER -