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Defending Attacks on Anti-Fraud Model With Generative Graph Representations
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AI
DOI：10.1109/TKDE.2025.3640634.png)
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Abstract
En 中文
Fraud detection is a typical data mining mission in the field of finance. In recent years, due to their capability of mining hidden associations between entities, graph neural networks (GNNs) have been widely applied to detect financial fraudsters. However, GNNs are fragile in their data aggregation process and will be attacked on purpose by fraudsters, therefore, some other pioneers have explored methods to enhance the robustness of GNN-based fraud detection models. But most existing models based on ideal settings, as real-life criminals tend to attack as far as they can reach, struggle to establish a unified effective approach for attacked and unattacked data of different scales in realistic scenarios. Furthermore, mainstream robust defense models indiscriminately modifying and truncating data will lose important information of the major unattacked parts in the original graph, which lowers their overall fraud detection precision. Therefore, in this work, we propose a novel generative fraud detection framework called GSRGNN. In particular, we first design a generative structure to obtain augmented node features. Then we prioritize the nodes with high degrees to create a more stable graph structure on local distributions. Finally, we pass the enhanced features and structure with the input ones in pairs through GNN layers, and create synthetic representations with abundant information and sufficient resistance to perturbations for subsequent fraud detection. In addition, we also design a novel black-box attack algorithm to realistically imitate the perturbations conducted by fraudsters on graph features and structure. Experiments on the world's leading electronic trading platform and public anti-fraud datasets demonstrate the outstanding performance of our proposed method compared with those state-of-the-art models, showing its superiority in precision and robustness on financial fraud detection missions.
Keywords:
Generative graph learning
financial fraud detection
adversarial attack and defense
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