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English(EN) Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions

AI 框架 SLEID 增强了以太坊 DeFi 中非法账户的检测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 SLEID，一个旨在检测以太坊去中心化金融 (DeFi) 生态系统中非法账户的新型框架。该自学习集成系统利用隔离森林模型和自训练机制为未标记数据生成伪标签，从而提高检测准确性。涉及超过 690 万笔以太坊交易的实验表明，SLEID 在精确率和 F1 分数方面优于现有方法，尤其是在识别少数非法类别方面，同时显著减少了对标记数据的需求。 AI

影响 通过减少对标记数据的依赖来提高非法账户的检测能力，从而增强 DeFi 的安全性。

排序理由 学术论文，详细介绍了检测以太坊 DeFi 交易中非法账户的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI 框架 SLEID 增强了以太坊 DeFi 中非法账户的检测能力

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学术论文，详细介绍了检测以太坊 DeFi 交易中非法账户的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shabnam Fazliani, Mohammad Mowlavi Sorond, Arsalan Masoudifard ·

    利用基于集成学习的半监督学习检测以太坊 DeFi 交易中的非法账户

    arXiv:2412.02408v3 Announce Type: replace-cross Abstract: 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 accompanie…

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