Work

TGAT-MPGCN: Multi-Graph Attention Message Passing for Direction-Aware Phishing Detection in Ethereum

Year: 2025

Type: article

Abstract: With the wide application of blockchain technology in finance, IoT, healthcare, and other fields, phishing scams have emerged as a growing security threat. Existing detection methods often lack in-depth modeling of the directional properties of transaction flows and struggle to effectively capture diverse transaction behaviors, directional relationships, and key neighbor dependencies. To address these limitations, we propose TGAT-MPGCN, a direction-aware phishing detection model that constructs three complementary first-order subgraphs, a sending graph, a receiving graph, and a bidirectional graph to explicitly capture transaction directionality. By integrating a graph-attention mechanism with weighted neighbor aggregation, the model enhances feature learning. Experimental evaluations on an Ethereum transaction dataset demonstrate the superior performance of our approach, achieving an accuracy of 97.21%, an AUC of 0.9721, an F1-score of 0.9719, a recall of 0.9629, and a precision rate of 98.11%, significantly outperforming traditional detection methods. This study offers a practical and scalable solution for accurate phishing detection in blockchain transaction networks.

Source: Blockchain Research and Applications

DOI: 10.1016/j.bcra.2025.100419

Authors: Jianlong Xu, Canjia Xu, Rongtao Zhang, Feixiang Diao, Zhi Xiong

Institution: Shantou University

Language: English

Topic: Spam and Phishing Detection

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,600

Funders: Science and Technology Planning Project of Guangdong Province, Basic and Applied Basic Research Foundation of Guangdong Province