Abstract:

With the rapid evolution of blockchain technology, the detection of anomalous on-chain transactions has emerged as a critical challenge for securing digital assets. However, current methods struggle to capture the complex topology and dynamic timing of transaction networks, resulting in limited detection accuracy. This paper proposed a blockchain anomaly transaction detection method based on the temporal graph attention network(TGAT). The approach introduced a behavioral paradigm-driven “temporal-structural” coupled modeling framework that utilized sine-cosine temporal encoding to synchronously quantify transaction timing and interaction topology, thereby enabling the precise identification of dynamic anomaly patterns. Furthermore, a multi-granularity attention optimization mechanism was designed to learn diverse behavioral patterns—such as fund convergence and chain-like dispersion—in parallel, significantly enhancing feature extraction precision in complex environments. Experimental results demonstrate that the proposed model substantially outperforms baseline methods in core metrics including precision, recall, and F1-score, with the F1-score improving by over 10%. Ablation studies verify the critical contributions of temporal encoding and multi-head attention mechanisms to performance enhancement, while highlighting the computational efficiency of the three-layer network architecture. This work provides an intelligent technical pathway for financial compliance scenarios, such as anti-money laundering and fraud detection, and possesses significant practical implications for the industry.

Key words: blockchain, financial security, anomaly detection, temporal graph neural network