Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain
This addresses security and trust issues for users and developers in the Ethereum ecosystem, though it appears incremental as it builds on existing graph learning methods.
The paper tackled the problem of detecting scams in Ethereum smart contracts by using graph representation learning to identify malicious transaction patterns, achieving reliable and accurate results with MLP outperforming GCN in tests.
As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts, that is, to represent Ethereum transaction data as graphs, and then use advanced ML technology to obtain reliable and accurate results. Taking into account the sample imbalance, we treated with SMOTE-ENN and tested several models, in which MLP performed better than GCN, but the exact effect depends on its field trials. Our research opens up more possibilities for trust and security in the Ethereum ecosystem.