TY - GEN
T1 - Detecting Scam Tokens and Backdoor Functions in EVM Based Networks
AU - Lorenzo, Sergio Anguita
AU - Miguel-Alonso, Jose
AU - Gomez-Goiri, Aitor
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space.
AB - The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space.
KW - Blockchain
KW - EVM
KW - Phishing-detection
KW - Scam
UR - http://www.scopus.com/inward/record.url?scp=105000624966&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-81928-5_28
DO - 10.1007/978-3-031-81928-5_28
M3 - Conference contribution
AN - SCOPUS:105000624966
SN - 9783031819278
T3 - Lecture Notes in Networks and Systems
SP - 288
EP - 298
BT - Blockchain and Applications, 6th International Congress
A2 - Prieto, Javier
A2 - Vargas, Rafael Pastor
A2 - Lage, Oscar
A2 - Machado, José Manuel
A2 - Bálint, Molnár
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024
Y2 - 26 June 2024 through 28 June 2024
ER -
