Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain

TL;DR

This paper introduces a graph representation learning approach to detect scams in Ethereum smart contracts by analyzing transaction patterns, addressing sample imbalance, and testing multiple models to improve security and trust in blockchain.

Contribution

It proposes a novel graph-based method for scam detection in Ethereum, utilizing advanced ML techniques and handling class imbalance for improved accuracy.

Findings

MLP outperformed GCN in detection accuracy

Graph representation effectively captures transaction patterns

Addressed sample imbalance with SMOTE-ENN

Abstract

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.