DeFiTrust: A transformer-based framework for scam DeFi token detection using event logs and sentiment analysis

Abstract

Decentralized finance (DeFi) token scams are rising, leading to significant losses for investors and users. Hence, it is vital to identify malicious DeFi tokens accurately and early to protect investor funds. Current Machine Learning models that detect scam tokens rely entirely on DeFi token transactional behavior and have overlooked the public opinion that can be derived from news and social media posts, which can provide helpful information for identifying scam tokens. Further, existing frameworks to detect scam tokens fail to explain why certain tokens are identified as scams, hindering stakeholder confidence in such systems. This paper proposes a new transformer-based framework called “DeFiTrust” to identify malicious DeFi tokens using transaction events and social media posts. DeFiTrust has two components: event log processing and sentiment analysis. The former creates a feature vector representing temporal variations in the event logs of a token. The latter analyzes social media posts to determine public opinion about the token. A fully-connected neural network then processes both pieces of information to determine whether the token is legitimate or a scam. We evaluated the proposed model against existing Machine Learning-based models using real-world data. The results demonstrate that DeFiTrust outperforms existing approaches and detects scam tokens earlier. Explanations generated using Integrated Gradients reveal that DeFiTrust identifies scam tokens based on valid reasons, enhancing stakeholder trust in the proposed model.