基于动态图神经网络的Tor节点分类方法OA

Tor(The onion router)is an anonymous communication network based on multi-layer encryption and distributed routing technology,which is widely used for privacy protection.However,its high anonymity also renders the network a breeding ground for Darknet activities,posing severe threats to national security and social stability.Due to the diversity of node functions and network complexity,effective node classifica-tion has become a critical research topic.This paper proposes a Tor node classification method utilizing Dy-namic Self-Attention Temporal Graph Neural Networks(DySAT).This approach analyzes the historical as-sociation graph of Tor relay nodes and employs a spatiotemporal dual-attention mechanism to simultaneously capture node performance and security indicators.Experiments validate the effectiveness of the proposed method by selecting high-quality nodes and benchmarking performance against normal circuits.Compared with Tor's default circuit construction algorithm,the probability of selecting malicious nodes is reduced from 6.2%to 1.8%,and the average latency drops from 0.478 s to 0.389 s.Consequently,this method provides a new technical approach for Darknet governance,vulnerable node identification,and mitigation,helping to enhance network security capabilities and contain illegal activities on the Darknet.