Leveraging Machine Learning for Multichain DeFi Fraud Detection
Georgios Palaiokrassas Sandro Scherrers Iason Ofeidis Leandros Tassiulas
Since the inception of permissionless blockchains with Bitcoin in 2008, it
became apparent that their most well-suited use case is related to making the
financial system and its advantages available to everyone seamlessly without
depending on any trusted intermediaries. Smart contracts across chains provide
an ecosystem of decentralized finance (DeFi), where users can interact with
lending pools, Automated Market Maker (AMM) exchanges, stablecoins,
derivatives, etc. with a cumulative locked value which had exceeded 160B USD.
While DeFi comes with high rewards, it also carries plenty of risks. Many
financial crimes have occurred over the years making the early detection of
malicious activity an issue of high priority. The proposed framework introduces
an effective method for extracting a set of features from different chains,
including the largest one, Ethereum and it is evaluated over an extensive
dataset we gathered with the transactions of the most widely used DeFi
protocols (23 in total, including Aave, Compound, Curve, Lido, and Yearn) based
on a novel dataset in collaboration with Covalent. Different Machine Learning
methods were employed, such as XGBoost and a Neural Network for identifying
fraud accounts detection interacting with DeFi and we demonstrate that the
introduction of novel DeFi-related features, significantly improves the
evaluation results, where Accuracy, Precision, Recall, F1-score and F2-score
where utilized.