Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions
Shabnam Fazliani, Mohammad Mowlavi Sorond, Arsalan Masoudifard, Shaghayegh Fazliani
智能合约的出现使去中心化金融(DeFi)在以太坊区块链上的迅速崛起,为金融创新和包容性提供了可观的回报。 然而,这种增长伴随着严重的安全风险,如从事欺诈的非法账户。 有效检测进一步受到标签数据稀缺和恶意帐户不断发展的策略的限制。 为了通过强大的解决方案来应对这些挑战,我们提出了基于自我学习的基于非法帐户检测的SLEID框架。 SLEID使用隔离森林模型进行初始异常检测和自我训练机制,为未标记的帐户迭代生成伪标签,从而提高检测准确性。 对6,903,860个具有广泛DeFi交互覆盖的以太坊交易的实验表明,SLEID显着优于监督和半监督基线,具有+2.56个百分点的精度,可比召回和+0.90个百分点的F1 - 特别是对于少数非法类别 - 以及更高的+3.74个百分点的准确性和改进PR-AUC,同时大大减少了对标记数据的依赖。
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 ecosyst...