Phishing fraud identity inference based on temporal transaction graph attention network

Abstract Blockchain technology has rapidly evolved due to its decentralization and traceability; however, its inherent anonymity also facilitates phishing fraud, resulting in substantial financial losses. Existing graph-based phishing detection methods often underexploit temporal transaction dynamics and rely on relatively shallow spatio-temporal feature fusion. To address these limitations, we propose a Temporal transaction graph attention network (TTGAN) for phishing account identification. First, an attributed transaction multigraph is constructed to model transactional interactions, explicitly preserving duplicate edges with associated timestamps and transaction values. Second, a temporal random walk module is designed to capture temporal dependencies, where the maximum walk length is adaptively determined by the transaction graph scale, and node sampling follows a time-biased exponential probability scheme; the temporal decay effect is implicitly incorporated through this design. In parallel, a graph attention mechanism module learns spatial representations by jointly modeling node and edge attributes. Finally, temporal and spatial features are fused via concatenation, followed by average pooling and a fully connected layer for account classification. Experiments conducted on four real-world Ethereum datasets demonstrate that TTGAN achieves a precision of 91.23%–96.39%, a recall of 94.41%–95.99%, and an F1-score of 92.20%–96.14%, significantly outperforming state-of-the-art methods, including Graph Attention Networks (GAT), temporal transaction subgraph network, and Graphormer.