一句话：提出基于图的自动化 MEV 检测系统，在 2100 万区块上识别 1210 万次 MEV 活动并揭示新型风险。
该论文针对现有工业级 MEV 检测依赖启发式规则、准确率有限且难以适应策略演化的问题，提出一种自动化软件工程方案：以基于图的盈利识别算法替代僵化规则，构建大规模 MEV 检测流水线。作者在 2100 万个以太坊区块、25 亿笔交易上评估，覆盖 The Merge 和 Proposer-Builder Separation 等基础设施转变；套利检测假阳性率为 0.6%，假阴性率为 2.4%。系统识别出 1210 万次 MEV 活动，包括 120 万次此前不可检测的高级变体，并揭示私有交易架构、builder-searcher 合谋和中心化趋势等对平台合规与系统风险的影响。
Maximal Extractable Value (MEV) activities pose critical operational challenges for blockchain enterprises, requiring automated detection systems to maintain platform integrity and regulatory compliance. Current industrial practices rely on heuristic rule-based methods with substantial accuracy limitations and inability to adapt to evolving MEV strategies. This paper presents an automated software engineering solution for large-scale MEV detection, introducing a novel graph-based profitability identification algorithm that replaces inflexible heuristic rules with adaptive mechanisms. Our automated system achieves 0.6% false positive rates for arbitrage detection and 2.4% false negative rates, significant improvements over existing methods with much higher error rates. We validate our approach on 21 million Ethereum blocks containing 2.5 billion transactions, covering critical infrastructure transitions including The Merge and Proposer-Builder Separation. Our automated pipeline identifies 12.1 million MEV activities, including 1.2 million previously undetectable advanced variants that pose emerging risks to platform operators. Key findings provide actionable insights for blockchain enterprises: private transaction architectures protect 71.4% of low-yield MEV opportunities rather than harming participants, contradicting previous assumptions. However, we identify concerning builder-searcher collusion involving 2,000+ transactions worth 350 ETH, highlighting compliance risks. Additionally, intensifying centralization trends show a single oligopoly controlling 43.1% of MEV activities in 2024, presenting systemic risks. Our automated detection framework provides blockchain enterprises with production-ready tools for MEV monitoring, risk assessment, and compliance management while offering critical insights for infrastructure design decisions in rapidly evolving DeFi environments.
DOI 原文 ·
@inproceedings{paperbot9530,
title = {The Gold Digger in the Dark Forest: Industrial-Scale MEV Analysis in Ethereum},
author = {Ningyu He and Tianyang Chi and Xiaohui Hu and Haoyu Wang},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00303}
}