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English(EN) On-Chain Behavioral Signatures for Classifying Centralized Exchange Addresses on Ethereum Using Proof-of-Reserves

新方法使用链上行为识别以太坊上的加密货币交易所地址

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种通过分析链上行为来识别以太坊区块链上中心化交易所（CEX）地址的新方法。该研究利用了15家中心化交易所和五个非中心化交易所类别的储备证明披露，提取了58种行为特征。在730天的时间窗口内，使用17个选定特征，LightGBM模型显著优于逻辑回归模型，其宏观PR-AUC达到了0.9594，而逻辑回归模型为0.7697。通过SHAP分析识别出的关键预测信号包括Gas行为、ERC-20代币活动和内部交易，为区块链监控提供了一种更具可解释性的方法。 AI

影响 提供了一种更具可解释性和准确性的方法来识别区块链上的加密货币交易所地址，可能有助于金融监控和安全。

排序理由 学术论文，详细介绍了新的分类框架和模型性能。[lever_c_demoted from research: ic=1 ai=0.7]

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 →

新方法使用链上行为识别以太坊上的加密货币交易所地址

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学术论文，详细介绍了新的分类框架和模型性能。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Seongchan Park, Hyunho Jeon, Byungsoo Kang, Gaeun Kim, Minseob So, Seongyong Kim ·

    On-Chain Behavioral Signatures for Classifying Centralized Exchange Addresses on Ethereum Using Proof-of-Reserves

    arXiv:2610.41888v1 Announce Type: cross Abstract: Centralized exchanges (CEXs) are key gateways between traditional finance and digital asset markets, yet identifying their blockchain addresses remains challenging. Existing approaches often rely on opaque attribution labels or tr…

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