  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.
