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Ethereum Phishing Scam Detection Based on Data Augmentation Method and Hybrid Graph Neural Network Model.

Zhen Chen1,2, Sheng-Zheng Liu1,2, Jia Huang1,2

  • 1College of Information Science Technology, Hainan Normal University, Haikou 571158, China.

Sensors (Basel, Switzerland)
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces DA-HGNN, a novel method for detecting Ethereum phishing scams by enhancing data and using a hybrid graph neural network. The model significantly improves detection accuracy, securing cryptocurrency transactions.

Keywords:
DA-HGNNEthereumblockchaindata augmentationphishing scam detection

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Area of Science:

  • Blockchain Technology
  • Cybersecurity
  • Machine Learning

Background:

  • Blockchain advancements have boosted cryptocurrency markets but also enabled illicit activities like phishing scams on platforms like Ethereum.
  • Existing phishing detection methods struggle with imbalanced datasets and effective feature extraction, compromising cryptocurrency transaction security.

Purpose of the Study:

  • To propose an efficient Ethereum phishing scam detection system to enhance the security and reliability of cryptocurrency transactions.
  • To address the limitations of existing methods in handling sample imbalance and feature extraction for blockchain phishing detection.

Main Methods:

  • Developed DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model) using basic node features and a sliding window sampling method for data augmentation.
  • Employed Conv1D and GRU-MHA for temporal feature extraction and a Graph Autoencoder with SAGEConv for learning structural features from transaction graph nodes.
  • Integrated temporal, basic, and embedding features for final phishing fraud node identification.

Main Results:

  • The DA-HGNN model achieved high performance on a real Ethereum dataset, with an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.994.
  • Achieved a Recall of 0.995 and an F1-score of 0.994, demonstrating superior effectiveness compared to existing methods and baseline models.
  • The data augmentation strategy effectively balanced samples, while the hybrid GNN model successfully extracted crucial temporal and structural features.

Conclusions:

  • The proposed DA-HGNN model offers a robust and effective solution for detecting Ethereum phishing scams, significantly outperforming current approaches.
  • This research contributes to enhancing the security of the cryptocurrency ecosystem by providing a more reliable method for identifying and mitigating phishing threats.
  • The integration of data augmentation and a hybrid graph neural network framework presents a promising direction for future cybersecurity research in blockchain.