# Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions

arXiv: https://arxiv.org/abs/2412.02408 · HTML: https://mlsanity.com/paper/2412.02408/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [Ethereum Fraud Detection with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2203.12363/) (2022)
- [An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities](https://mlsanity.com/paper/2106.06389/) (2021)
- [Analysis of Cryptocurrency Transactions from a Network Perspective: An Overview](https://mlsanity.com/paper/2011.09318/) (2020)
- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [Multi-Environment Meta-Learning in Stochastic Linear Bandits](https://mlsanity.com/paper/2205.06326/) (2022)
- [Leveraging Machine Learning for Multichain DeFi Fraud Detection](https://mlsanity.com/paper/2306.07972/) (2023)
- [Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation](https://mlsanity.com/paper/2307.13978/) (2023)
- [An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies](https://mlsanity.com/paper/2101.03141/) (2020)
- [Does Money Laundering on Ethereum Have Traditional Traits?](https://mlsanity.com/paper/2303.15841/) (2023)
- [Femtosecond CDMA Using Dielectric Metasurfaces: Design Procedure and Challenges](https://mlsanity.com/paper/1712.00834/) (2017)
- [Semi-Supervised Learning for Anomaly Detection in Blockchain-based Supply Chains](https://mlsanity.com/paper/2407.15603/) (2024)

## Built on by (most cited first, 1 of 1)

- [Enhancing Physics-Informed Neural Networks Through Feature Engineering](https://mlsanity.com/paper/2502.07209/) (2025)

## Abstract (arXiv)

The advent of smart contracts has enabled the rapid rise of Decentralized Finance (DeFi) on the Ethereum blockchain, offering substantial rewards in financial innovation and inclusivity. This growth, however, is accompanied by significant security risks such as illicit accounts engaged in fraud. Effective detection is further limited by the scarcity of labeled data and the evolving tactics of malicious accounts. To address these challenges with a robust solution for safeguarding the DeFi ecosystem, we propose SLEID, a Self-Learning Ensemble-based Illicit account Detection framework. SLEID uses an Isolation Forest model for initial outlier detection and a self-training mechanism to iteratively generate pseudo-labels for unlabeled accounts, enhancing detection accuracy. Experiments on 6,903,860 Ethereum transactions with extensive DeFi interaction coverage demonstrate that SLEID significantly outperforms supervised and semi-supervised baselines with +2.56 percentage-point precision, comparable recall, and +0.90 percentage-point F1 -- particularly for the minority illicit class -- alongside +3.74 percentage-points higher accuracy and improvements in PR-AUC, while substantially reducing reliance on labeled data.