区块链技术的匿名性与价值传递特性可能被恶意攻击者利用以实施网络钓鱼或其他欺诈行为。虽然链上数据公开、透明、可追溯，但是攻击者仍可通过设计复杂的交易链路，使资产在众多账户之间进行流转。最终，这些资产可能会被集中至某交易所账户并被提取，从而实现非法的利益获取。针对上述问题，面向以太坊提出一种价值驱动的交易追踪排名方法。首先收集12起诈骗金额超过百万美元的以太坊攻击案例，获取大小为27 GB的交易数据，构建地址图；然后从链上抽取代币的流动池数据，计算代币历史价格，确定地址图中各交易的权重系数；最后提出基于价值占比的动态残差放缩机制，优化地址图结构，更加偏向主要的价值流通路径。实验结果表明，召回率可达89.24%，相较于交易追踪排名（transaction tracingrank,TTR）、APPR和Haircut算法分别提高了7%、20%和37%，验证了本文方法在检测欺诈账户上的高效性和准确性。
Blockchain offers users anonymity and facilitates the decentralized transfer of value. However, malicious attackers might employ phishing or other fraudulent methods to steal assets and withdraw them from cryptocurrency exchanges by designing complex transaction interactions. In this paper, we address this challenge by presenting a valuedriven transaction tracking and ranking method tailored for Ethereum. In this approach, we collect a transaction dataset of up to 27 GB from 12 Ethereum attack cases with fraud amounts exceeding one million US dollars, and construct an address graph to describe the relationship between addresses. We then invoke token liquidity pool data from the onchain data to represent the historical price of assets and determine the weight coefficients for transactions in the graph. Finally, we introduce a dynamic residual scaling mechanism based on value proportion to optimize the address graph structure by optimal value flow paths. Experimental results show that the proposed method achieves a recall rate of 89.24%, which represents a notable improvement of 7%, 20%, and 37% over transaction tracing rank (TTR), APPR, and Haircut algorithms, respectively, confirming the effectiveness of the proposed method.
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