用集成自学习方法提升以太坊非法账户检测精度，减少对标注数据依赖。
Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions
- 结合孤立森林与自训练机制，通过伪标签迭代提升检测能力。
- 在690万交易上实现精度提升2.56个百分点，少数类检测效果显著。
- 适合关注区块链安全、少标注场景下的异常检测研究者使用。
智能合约的兴起推动了以太坊上去中心化金融（DeFi）的快速发展，带来了金融创新与包容性的巨大收益。然而，这一增长也伴随严重的安全风险，如从事欺诈的非法账户问题。有效的检测面临标注数据稀缺和恶意账户策略不断演变的双重挑战。为提供一种稳健的解决方案以保护DeFi生态系统，我们提出SLEID（Self-Learning Ensemble-based Illicit account Detection）框架。SLEID首先使用孤立森林模型进行初始异常检测，并采用自训练机制迭代生成未标记账户的伪标签，从而提升检测精度。在覆盖广泛DeFi交互的6,903,860条以太坊交易数据上的实验表明，SLEID显著优于监督与半监督基线方法，实现+2.56百分点的精度提升，召回率相当，且F1分数提高+0.90百分点——尤其在少数类非法账户上表现突出；同时准确率提升+3.74百分点，并在PR-AUC上取得改进，且大幅降低对标注数据的依赖。
原文摘要 · Abstract (English)
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 $\textbf{SLEID}$, a $\textbf{S}$elf-$\textbf{L}$earning $\textbf{E}$nsemble-based $\textbf{I}$llicit account $\textbf{D}$etection 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 $\textbf{+2.56}$ percentage-point precision, comparable recall, and $\textbf{+0.90}$ percentage-point F1 -- particularly for the minority illicit class -- alongside $\textbf{+3.74}$ percentage-points higher accuracy and improvements in PR-AUC, while substantially reducing reliance on labeled data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。