Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors

TL;DR

This paper introduces MonTi, a transformer-based attack model that effectively simulates multi-target graph injection attacks against GNN-based fraud detectors, revealing significant vulnerabilities in real-world scenarios.

Contribution

The paper presents MonTi, a novel transformer-based approach for multi-target graph injection attacks, improving attack effectiveness over existing methods and highlighting security risks in GNN-based fraud detection.

Findings

MonTi outperforms state-of-the-art attack methods on five real-world datasets.

Multi-target injection attacks can significantly degrade GNN-based fraud detection accuracy.

Adaptive degree allocation enhances attack diversity and effectiveness.

Abstract

Graph neural networks (GNNs) have emerged as an effective tool for fraud detection, identifying fraudulent users, and uncovering malicious behaviors. However, attacks against GNN-based fraud detectors and their risks have rarely been studied, thereby leaving potential threats unaddressed. Recent findings suggest that frauds are increasingly organized as gangs or groups. In this work, we design attack scenarios where fraud gangs aim to make their fraud nodes misclassified as benign by camouflaging their illicit activities in collusion. Based on these scenarios, we study adversarial attacks against GNN-based fraud detectors by simulating attacks of fraud gangs in three real-world fraud cases: spam reviews, fake news, and medical insurance frauds. We define these attacks as multi-target graph injection attacks and propose MonTi, a transformer-based Multi-target one-Time graph injection attack model. MonTi simultaneously generates attributes and edges of all attack nodes with a transformer encoder, capturing interdependencies between attributes and edges more effectively than most existing graph injection attack methods that generate these elements sequentially. Additionally, MonTi adaptively allocates the degree budget for each attack node to explore diverse injection structures involving target, candidate, and attack nodes, unlike existing methods that fix the degree budget across all attack nodes. Experiments show that MonTi outperforms the state-of-the-art graph injection attack methods on five real-world graphs.