用图注意力网络提升社交网络中的僵尸账号检测精度。
Sybil Detection using Graph Neural Networks
- 基于图注意力机制动态分配节点权重，融合已知恶意与正常节点信息。
- 在高攻击复杂度下准确率超越现有方法，真实数据集表现稳定。
- 适用于多种网络规模，仅需结构信息即可部署，适合平台安全防护。
本文提出SYBILGAT，一种基于图注意力网络（GAT）的新型僵尸账号检测方法。传统方法依赖网络结构特征，但在大量攻击边存在时性能下降，且难以同时利用已知恶意和正常节点。SYBILGAT通过动态分配聚合过程中的注意力权重，提升检测效果。我们在采样子图预训练、合成网络及针对性攻击场景下进行了广泛实验。结果表明，该方法在攻击复杂度高、攻击边数量增加时显著优于当前最优算法，对不同网络模型和规模均保持稳健表现。我们成功将模型应用于包含超过269,000个节点和680万条边的真实Twitter图。SYBILGAT具备良好的灵活性与泛化能力，仅需结构信息即可有效防御在线社交网络中的僵尸攻击。
原文摘要 · Abstract (English)
This paper presents SYBILGAT, a novel approach to Sybil detection in social networks using Graph Attention Networks (GATs). Traditional methods for Sybil detection primarily leverage structural properties of networks; however, they tend to struggle with a large number of attack edges and are often unable to simultaneously utilize both known Sybil and honest nodes. Our proposed method addresses these limitations by dynamically assigning attention weights to different nodes during aggregations, enhancing detection performance. We conducted extensive experiments in various scenarios, including pretraining in sampled subgraphs, synthetic networks, and networks under targeted attacks. The results show that SYBILGAT significantly outperforms the state-of-the-art algorithms, particularly in scenarios with high attack complexity and when the number of attack edges increases. Our approach shows robust performance across different network models and sizes, even as the detection task becomes more challenging. We successfully applied the model to a real-world Twitter graph with more than 269k nodes and 6.8M edges. The flexibility and generalizability of SYBILGAT make it a promising tool to defend against Sybil attacks in online social networks with only structural information.
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