Paper: arXiv 2306.07972

Authors: Georgios Palaiokrassas, Sandro Scherrers, Iason Ofeidis, Leandros Tassiulas

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a strong empirical contribution by leveraging a novel multi-chain dataset across 23 major DeFi protocols to detect fraudulent accounts using machine learning. While the mathematical formulations are minimal, focusing mostly on standard ML classifiers and evaluation metrics, the practical application and feature engineering offer valuable real-world utility.

Research Flowchart

  flowchart TD
    A[Goal: Multichain DeFi Fraud Detection] --> B[Data Gathering: 23 DeFi Protocols via Covalent]
    B --> C[Feature Engineering: Novel DeFi-Specific Features]
    C --> D[ML Model Training: XGBoost & Neural Networks]
    D --> E[Model Evaluation: Accuracy, Precision, Recall, F1/F2]
    E --> F[Outcome: Significant Performance Boost in Fraud Account Detection]