MHTGR: Multi-Modal Hierarchical Temporal Graph Representation Learning for Ethereum Phishing Detection

Abstract

Ethereum's swift development has elevated phishing scams to primary security concerns within blockchain networks. Current detection methods face three key challenges: insufficient hierarchical temporal modeling, inadequate pattern-aware structural recognition, and the lack of effective mechanisms to integrate multi-modal information. This paper presents an innovative approach for phishing detection using Multi-modal Hierarchical Temporal Graph Representation (MHTGR). Our method analyzes phishing behaviors by jointly considering temporal dynamics and structural topology of transaction data. First, we construct Hierarchical Transaction Graph Network (HTGN) to organize raw transaction records into structured graph representations. Then, multiple feature modalities are extracted through a Parallel Feature Extraction (PFE) module. Finally, these features are integrated via a Multi-modal Fusion (MMF) module for comprehensive phishing detection. Empirical evaluations conducted across multiple datasets from Ethereum demonstrate that the proposed method outperforms existing methods, providing effective solutions towards blockchain security.