The detection of Maximal Extractable Value (MEV) in blockchain is crucial for enhancing blockchain security, as it enables the evaluation of potential consensus layer risks, the effectiveness of anti-centralization solutions, and the assessment of user exploitation. However, existing MEV detection methods face limitations due to their low recall rate, reliance on pre-registered Application Binary Interfaces (ABIs) and the need for continuous monitoring of new DeFi services. In this paper, we propose ArbiNet, a novel GNN-based detection model that offers a low-overhead and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs. We collected an extensive MEV dataset, surpassing currently available public datasets, to train ArbiNet. Our implemented model and open dataset enhance the understanding of the MEV landscape, serving as a foundation for MEV quantification and improved blockchain security.

翻译：区块链中最大可提取价值（MEV）的检测对提升区块链安全性至关重要，因为它能够评估潜在共识层风险、反中心化解决方案的有效性以及用户被利用的程度。然而，现有MEV检测方法存在局限性，包括召回率低、依赖预注册的应用二进制接口（ABI）以及需要持续监控新型DeFi服务。本文提出ArbiNet——一种新颖的基于图神经网络（GNN）的检测模型，该模型无需智能合约代码或ABI即可实现低开销且精确的MEV检测。我们收集了超越现有公开数据集的广泛MEV数据集用于训练ArbiNet。所实现的模型与开放数据集深化了对MEV格局的理解，为MEV量化及强化区块链安全奠定了基础。