Scam Detection in Ethereum Smart Contracts Using Graph Learning
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🕸️ Innovative Approach to Fraud Detection in Ethereum Smart Contracts Using Graph Representation Learning. A new study addresses the challenge of detecting fraudulent activities in Ethereum smart contracts by employing graph representation learning techniques. Traditional methods often struggle with scalability and adaptability, but this research transforms Ethereum invoice data into graph structures, enhancing the detection of fraudulent transactions. By utilizing machine learning models, particularly Multi-Layer Perceptron (MLP) and Graph Convolutional Networks (GCN), the study demonstrates that MLP outperforms GCN in categorization tasks. Additionally, the approach incorporates SMOTE-ENN techniques to tackle label imbalance, ultimately providing a scalable solution to bolster confidence and security within the Ethereum ecosystem.
