Unraveling the MEV Enigma: ABI-Free Detection Model using Graph Neural Networks

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 m...

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Bibliographic Details
Published inarXiv.org
Main Authors Park, Seongwan, Jeong, Woojin, Lee, Yunyoung, Son, Bumho, Jang, Huisu, Lee, Jaewook
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.05.2023
Subjects
Blockchain
Cryptography
Datasets
Graph neural networks
Security
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Summary: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.
ISSN:2331-8422