MEV Detection for Ethereum Security: An Empirical Analysis on Agreement and Scalability

Abstract

Maximal Extractable Value (MEV) poses significant threats to the security and fairness of Ethereum's decentralized finance ecosystem by enabling participants to exploit transaction ordering to extract profits at the expense of others. Heuristic-based detection methods have been widely adopted to identify MEV transactions such as sandwich, arbitrage, and liquidation. However, a lack of standardized evaluation across heuristics limits the ability to compare their detection behavior and computational characteristics. This paper presents a unified experimental framework to systematically evaluate the detection capabilities, agreement rates, and resource efficiency of existing heuristic approaches. An agreement metric is introduced to quantify consistency across detection methods. Additionally, we assessed resource utilization and execution time to evaluate computational scalability. Our empirical findings reveal that while these approaches exhibit scalability, their reliability, measured in terms of detection consistency, varies significantly across different MEV types. Agreement rates averaged 0.53 for sandwich detection, 0.40 for arbitrage, and 0.81 for liquidation, highlighting substantial differences in detection capabilities and heuristic formulations. These findings offer valuable insights into the practical challenges of achieving consistent MEV detection and highlight the need for developing more robust security countermeasures.