利用基于集成的半监督学习进行以太坊DeFi交易中的非法账户检测
Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions
AI总结：
本文提出SLEID框架，通过孤立森林模型和自训练机制提升非法账户检测精度，实验显示其在精度、F1值和AUC指标上均优于传统方法，尤其在少数类检测上表现突出。
摘要：
智能合约的出现推动了以太坊区块链上去中心化金融（DeFi）的快速发展，但随之而来的安全风险，如欺诈性账户，也日益严峻。由于标注数据稀缺和恶意账户战术的演变，有效检测面临挑战。为此，本文提出SLEID，一种自学习集成式非法账户检测框架。SLEID利用孤立森林模型进行初步异常检测，并通过自训练机制迭代生成未标注账户的伪标签，以提升检测精度。在6,903,860笔以太坊交易的实验中，SLEID在精度上比监督和半监督基线高出+2.56个百分点，召回率相当，F1值提高+0.90个百分点，尤其在少数类非法账户上表现突出，同时在准确率和PR-AUC指标上也显著提升，并大幅减少了对标注数据的依赖。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
