基于异构图的Tor网络关键节点识别方法

王楠楠; 黄诚; 刘骏以; 游畅; 时金桥

doi:10.12263/DZXB.20250881

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基于异构图的Tor网络关键节点识别方法

密态网络安全前沿技术 | 更新时间：2026-07-30

- 基于异构图的Tor网络关键节点识别方法
- Heterogeneous Graph-Based Identification of Critical Relays in Tor Networks
- 电子学报 2026年54卷第4期页码：1513-1528
- 作者机构：
  
  1.四川大学网络空间安全学院，四川成都 610064
  2.北京邮电大学网络空间安全学院，北京 100876
- 作者简介：
  
  王楠楠男，2001年2月生，山西晋城人。2025年硕士毕业于四川大学网络空间安全学院，现为成都市公安局高新分局民警。主要研究方向为网络公害治理与网络安全。 E-mail: ning000121@gmail.com
  黄诚男，1987年9月生，重庆云阳人。博士，现为四川大学网络空间安全学院教授，博士生导师。中国计算机学会高级会员。主要研究方向为网络空间安全，尤其是威胁情报、供应链检测和黑灰产治理。中国电子学会会员编号：E190184335M。 E-mail: codesec@scu.edu.cn
  刘骏以男，2002年5月生，重庆云阳人。现为四川大学网络空间安全学院在读硕士研究生。主要研究方向为匿名通信网络安全。 E-mail: 1325389290@qq.com
  游畅男，2001年4月生，四川成都人。四川大学网络空间安全学院硕士研究生。主要研究方向为匿名通信与网络安全。中国电子学会会员编号：E190109035M。 E-mail: 1162242952@qq.com
  时金桥男，1978年1月生，黑龙江哈尔滨人。博士，现为北京邮电大学网络空间安全学院教授、博士生导师。中国计算机学会会员。主要研究方向为网络与信息安全，尤其是隐私增强技术和数据泄露检测。 E-mail: shijingiao@bupt.edu.cn
- 基金信息：
  
  国家重点研发计划(2023YFB3106600)
- DOI：10.12263/DZXB.20250881
  中图分类号： TP393.08;
- 收稿：2025-11-22，
  
  录用：2026-01-06，
  
  网络首发：2026-03-30，
  
  纸质出版：2026-04-25
- 稿件说明：
移动端阅览
王楠楠, 黄诚, 刘骏以, 等. 基于异构图的Tor网络关键节点识别方法[J]. 电子学报, 2026, 54(04): 1513-1528.

WANG Nannan, HUANG Cheng, LIU Junyi, et al. Heterogeneous Graph-Based Identification of Critical Relays in Tor Networks[J]. Acta Electronica Sinica, 2026, 54(04): 1513-1528.
王楠楠, 黄诚, 刘骏以, 等. 基于异构图的Tor网络关键节点识别方法[J]. 电子学报, 2026, 54(04): 1513-1528. DOI：10.12263/DZXB.20250881

WANG Nannan, HUANG Cheng, LIU Junyi, et al. Heterogeneous Graph-Based Identification of Critical Relays in Tor Networks[J]. Acta Electronica Sinica, 2026, 54(04): 1513-1528. DOI：10.12263/DZXB.20250881

摘要

随着匿名通信需求的持续增长，Tor网络因其多条加密路由与去中心化架构，被广泛应用于隐私保护、反审查通信以及敏感信息传输等场景。然而，目前Tor网络中继节点数量增长缓慢、节点负载分布不均以及节点准入机制缺乏严格审查，使得部分高频参与路径构建的关键中继节点逐渐成为攻击者的重点操控目标。一旦这些节点被恶意控制，将显著削弱网络匿名性、破坏路径构建安全性，并对整体网络稳定性产生严重影响。因此，如何精准识别Tor网络中的关键中继节点，已成为提升匿名通信系统安全性与可靠性的核心问题。现有关键节点识别方法多基于静态拓扑结构指标或单一节点行为特征，难以有效刻画节点间复杂的隐式关系与多维语义联系，导致模型在面对真实动态网络环境时泛化能力与鲁棒性不足。针对上述问题，本文提出一种基于异构图建模与关系感知机制的无监督关键节点识别方法。首先，从节点稳定性、链路出现频次、功能标签及资源能力等多维属性出发，构建融合节点特征与隐式关系的多源异构图模型，实现对Tor网络结构的精细化表征；其次，引入关系感知异构注意力网络，对家族关系、自治系统归属关系、地理接近性关系以及路径共现关系等多类型边进行差异化建模，并通过异构注意力机制自适应融合不同关系语义信息，显著提升节点表示的判别性与鲁棒性；最后，在无监督学习框架下设计节点评分机制，实现关键中继节点的重要性排序与自动识别。基于真实Tor共识数据与实际链路构建数据开展系统实验评估，结果表明：在Top-100节点设置下，所提方法实现了85.0%的节点覆盖率，且识别节点的带宽比全网平均节点带宽高约25.9%。进一步实验表明：当移除模型识别出的关键节点后，网络整体带宽与链路覆盖率均显著下降，验证了所识别节点在网络运行中的核心作用。研究结果表明：本文方法能够有效刻画Tor网络中继节点的隐式关联结构，为匿名通信网络关键节点识别提供了一种新的建模范式，对提升Tor基础设施安全性具有重要理论意义与实践价值。

Abstract

With the continuous growth of demand for anonymous communication

the Tor network has been widely adopted in privacy protection

censorship circumvention

and sensitive information transmission scenarios due to its multi-hop encrypted routing and decentralized architecture. However

the slow growth of relay nodes

uneven load distribution

and the lack of strict scrutiny in relay admission mechanisms have caused certain high-frequency relays involved in path construction to gradually become prime targets for adversarial control. Once these relays are maliciously compromised

the anonymity of the network will be significantly weakened

the security of circuit construction will be undermined

and the overall network stability will be severely affected. Therefore

accurately identifying critical relays in the Tor network has become a core issue for enhancing the security and reliability of anonymous communication systems. Existing critical node identification methods mainly rely on static topological metrics or single behavioral features

which fail to effectively capture complex implicit relationships and multidimensional semantic associations among relays

resulting in limited generalization ability and robustness when facing real dynamic network environments. To address these challenges

this paper proposes an unsupervised critical relay identification method based on heterogeneous graph modeling and relation-aware mechanisms. First

a multi-source heterogeneous graph model is constructed by integrating multidimensional attributes such as relay stability

path occurrence frequency

functional labels

and resource capabilities

enabling a fine-grained representation of the Tor network structure. Then

a relation-aware heterogeneous attention network is introduced to differentially model multiple types of relations

including family relations

autonomous system affiliation

geographic proximity

and path co-occurrence. An heterogeneous attention mechanism is further employed to adaptively fuse diverse relational semantics

significantly enhancing the discriminative power and robustness of relay representations. Finally

an unsupervised scoring mechanism is designed to rank and automatically identify critical relays. Extensive experiments conducted on real Tor consensus data and practical circuit construction data demonstrate that

under the Top-100 relay setting

the proposed method achieves a relay coverage rate of 85.0%

and the bandwidth of the identified relays is approximately 25.9% higher than the network average. Further experiments show that removing the identified critical relays leads to a significant degradation in overall network bandwidth and circuit coverage

validating the core role of these relays in network operation. The results indicate that the proposed method effectively captures the implicit relational structure among Tor relays

providing a novel modeling paradigm for critical relay identification in anonymous communication networks and offering important theoretical and practical implications for enhancing the security of Tor infrastructure.

关键词

Keywords

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