SoK：跨链交易的识别与匹配
SoK: Cross-Chain Transaction Identification and Matching
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中文总结 AI 辅助
该SoK研究梳理了跨链交易识别与匹配的四类存提款识别方法、三种交易匹配机制，评估了现有数据集可用性并提炼失效模式，还提出了跨链分析的四个开放挑战。
中文摘要 AI 辅助
跨链桥、即时加密货币交易所以及集中式跨账本平台在日益多元化的多链生态系统中实现资产转移。然而，这些系统多次成为高价值攻击的目标，也是跨链洗钱的渠道。跨链交易比单链交易更难分析：没有单一账本记录完整的跨链转账，其证据分散在源链、目标链和链下系统中，且这些证据的可用性和可靠性在不同系统间差异巨大。本文提出了关于跨链交易识别与匹配的知识系统化（SoK）研究。首先，我们将存提款识别方法分为四类，将交易匹配方法分为三种机制：确定性标识符匹配、字段约束启发式方法和模型辅助匹配。我们发现，这些方法的适用性和报告性能主要受底层系统暴露的证据影响，还进一步研究了匹配对如何支持下游攻击检测和资金追踪。其次，我们评估了现有数据集和人工制品的可用性，发现不到一半仍可获取，并提炼出三种人工制品失效模式。最后，我们概述了实现可审计、可复现且可操作的跨链分析面临的四个开放挑战。
英文摘要
Cross-chain bridges, instant cryptocurrency exchanges, and centralized cross-ledger platforms move assets across an increasingly multi-chain ecosystem. However, these systems have repeatedly become targets of high-value attacks and channels for cross-chain money laundering. Cross-chain transactions are substantially harder to analyze than single-chain transactions: no single ledger records an entire cross-chain transfer, its evidence is scattered across the source chain, the destination chain, and off-chain systems, and the availability and reliability of that evidence vary widely across systems. In this paper, we present a systematization of knowledge (SoK) on cross-chain transaction identification and matching. First, we classify deposit and withdrawal identification methods into four approaches and transaction matching methods into three mechanisms: deterministic identifier matching, field-constraint heuristics, and model-assisted matching. We find that their applicability and reported performance are shaped mainly by the evidence the underlying system exposes, and we further examine how matched pairs support downstream attack detection and fund tracing. Second, we assess the availability of existing datasets and artifacts, finding that fewer than half remain obtainable, and distill three artifact failure modes. Finally, we outline four open challenges toward auditable, reproducible, and actionable cross-chain analysis.
