# Leveraging Machine Learning for Multichain DeFi Fraud Detection

arXiv: https://arxiv.org/abs/2306.07972 · HTML: https://mlsanity.com/paper/2306.07972/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 6 of 6)

- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users](https://mlsanity.com/paper/2005.14051/) (2020)
- [An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities](https://mlsanity.com/paper/2106.06389/) (2021)
- [SoK: Lending Pools in Decentralized Finance](https://mlsanity.com/paper/2012.13230/) (2020)
- [Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and Tornado Cash](https://mlsanity.com/paper/2201.06811/) (2022)
- [Of Degens and Defrauders: Using Open-Source Investigative Tools to Investigate Decentralized Finance Frauds and Money Laundering](https://mlsanity.com/paper/2303.00810/) (2023)

## Built on by (most cited first, 3 of 3)

- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions](https://mlsanity.com/paper/2412.02408/) (2024)
- [Generation of Optimized Solidity Code for Machine Learning Models using LLMs](https://mlsanity.com/paper/2503.06203/) (2025)

## Abstract (arXiv)

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.