Sybil Detection using Graph Neural Networks
It addresses the problem of Sybil attacks for online social networks, offering a robust and generalizable tool, though it is incremental as it builds on existing GAT methods.
This paper tackles Sybil detection in social networks by proposing SYBILGAT, a method using Graph Attention Networks, which significantly outperforms state-of-the-art algorithms, especially in high-attack scenarios and on a real-world Twitter graph with over 269k nodes.
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.