Heterogeneous Graph-Based Identification of Critical Relays in Tor Networks

WANG Nannan; HUANG Cheng; LIU Junyi; YOU Chang; SHI Jinqiao

doi:10.12263/DZXB.20250881

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Heterogeneous Graph-Based Identification of Critical Relays in Tor Networks

Cutting\-edge Technologies for Encrypted Network Security | 更新时间：2026-07-30

- Heterogeneous Graph-Based Identification of Critical Relays in Tor Networks
- ACTA ELECTRONICA SINICA Vol. 54, Issue 4, Pages: 1513-1528(2026)
- 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：
- 基金信息：
  
  National Key Research and Development Program of China(2023YFB3106600)
- DOI：10.12263/DZXB.20250881
  CLC： TP393.08;
- Received：22 November 2025，
  
  Accepted：06 January 2026，
  
  Online First：30 March 2026，
  
  Published：25 April 2026
- 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

references

中国互联网络信息中心 . 第 54 次中国互联网络发展状况统计报告 [R/OL ] . ( 2024-09-19 )[ 2025-11-10 ] . https://www.199it.com/archives/1718242.html https://www.199it.com/archives/1718242.html . DOI: 10.1007/978-3-662-57521-5_9 http://dx.doi.org/10.1007/978-3-662-57521-5_9

China Internet Network Information Center (CNNIC) . The 54th statistical report on China’s internet development [R/OL ] . ( 2024-09-19 )[ 2025-11-10 ] . https://www.199it.com/archives/1718242.html https://www.199it.com/archives/1718242.html . (in Chinese) . DOI: 10.1007/978-3-662-57521-5_9 http://dx.doi.org/10.1007/978-3-662-57521-5_9

Computer Crime Research Center . The cost of cybercrime to reach over $12tn by 2025 [EB/OL ] . ( 2024-01-24 )[ 2025-11-10 ] . https://www.crime-research.org/news/24.01.2024/4132/ https://www.crime-research.org/news/24.01.2024/4132/ .

Ventures Cybersecurity . Cybercrime to cost the world $10.5 trillion annually by 2025 [EB/OL ] . ( 2020-11-13 )[ 2025-11-10 ] . https://cybersecurityventures.com/cybercrime-damage-costs-10-trillion-by-2025/ https://cybersecurityventures.com/cybercrime-damage-costs-10-trillion-by-2025/ .

Dingledine R , Mathewson N , Syverson P . Tor: The second-generation onion router [C ] // Proceedings of the 13th Conference on USENIX Security Symposium . Berkeley : USENIX Association , 2004 : 21 . DOI: 10.21236/ada465464 http://dx.doi.org/10.21236/ada465464

The Tor Project . Tor metrics [EB/OL ] . ( 2010-11-30 )[ 2025-11-10 ] . https://metrics.torproject.org/userstats-relay-country.html https://metrics.torproject.org/userstats-relay-country.html .

The Tor Project . Tor metrics [EB/OL ] . ( 2010‑11‑30 )[ 2024-12-10 ] . https://metrics.torproject.org/networksize.html https://metrics.torproject.org/networksize.html .

Bauer K , McCoy D , Grunwald D , et al . Low-resource routing attacks against tor [C ] // Proceedings of the 2007 ACM Workshop on Privacy in Electronic Society . New York : ACM , 2007 : 11 - 20 . DOI: 10.1145/1314333.1314336 http://dx.doi.org/10.1145/1314333.1314336

Greubel A , Pohl S , Kounev S . Quantifying measurement quality and load distribution in tor [C ] // Proceedings of the 36th Annual Computer Security Applications Conference . New York : ACM , 2020 : 129 - 140 . DOI: 10.1145/3427228.3427238 http://dx.doi.org/10.1145/3427228.3427238

Jansen R , Johnson A . On the accuracy of tor bandwidth estimation [C ] // Proceedings of the 22nd International Conference on Passive and Active Measurement . Heidelberg : Springer , 2021 : 481 - 498 . DOI: 10.1007/978-3-030-72582-2_28 http://dx.doi.org/10.1007/978-3-030-72582-2_28

Zhang Junlong , Luo Yu . Degree centrality, betweenness centrality, and closeness centrality in social network [C ] // Proceedings of the 2nd International Conference on Modelling, Simulation and Applied Mathematics . Atlantis Press , 2017 : 300 - 303 . DOI: 10.2991/msam-17.2017.68 http://dx.doi.org/10.2991/msam-17.2017.68

Al-Nabki M W , Fidalgo E , Alegre E , et al . ToRank: Identifying the most influential suspicious domains in the Tor network [J ] . Expert Systems with Applications , 2019 , 123 : 212 - 226 . DOI: 10.1016/j.eswa.2019.01.029 http://dx.doi.org/10.1016/j.eswa.2019.01.029

Srinivas A , Velusamy R L . Identification of influential nodes from social networks based on Enhanced Degree Centrality Measure [C ] // 2015 IEEE International Advance Computing Conference . Piscataway : IEEE , 2015 : 1179 - 1184 . DOI: 10.1109/iadcc.2015.7154889 http://dx.doi.org/10.1109/iadcc.2015.7154889

Song Zhichao , Duan Hong , Ge Yuanzheng , et al . A novel measure of centrality based on betweenness [C ] // 2015 Chinese Automation Congress . Piscataway : IEEE , 2016 : 174 - 178 . DOI: 10.1109/cac.2015.7382491 http://dx.doi.org/10.1109/cac.2015.7382491

Kianian S , Rostamnia M . An efficient path-based approach for influence maximization in social networks [J ] . Expert Systems with Applications , 2021 , 167 : 114168 . DOI: 10.1016/j.eswa.2020.114168 http://dx.doi.org/10.1016/j.eswa.2020.114168

Zhong Linfeng , Shang Mingsheng , Chen Xiaolong , et al . Identifying the influential nodes via eigen-centrality from the differences and similarities of structure [J ] . Physica A: Statistical Mechanics and its Applications , 2018 , 510 : 77 - 82 . DOI: 10.1016/j.physa.2018.06.115 http://dx.doi.org/10.1016/j.physa.2018.06.115

Harooni A , Lotfi-Shahreza M , Shams M , et al . Centrality in multilayer networks: Accurate measurements with MultiNetPy [J ] . The Journal of Supercomputing , 2025 , 81 ( 5 ): 689 . DOI: 10.1007/s11227-025-07197-8 http://dx.doi.org/10.1007/s11227-025-07197-8

Stolarski M , Piróg A , Bródka P . Identifying key nodes for the influence spread using a machine learning approach [J ] . Entropy , 2024 , 26 ( 11 ): 955 . DOI: 10.3390/e26110955 http://dx.doi.org/10.3390/e26110955

Asgharian Rezaei A , Munoz J , Jalili M , et al . A machine learning-based approach for vital node identification in complex networks [J ] . Expert Systems with Applications , 2023 , 214 : 119086 . DOI: 10.1016/j.eswa.2022.119086 http://dx.doi.org/10.1016/j.eswa.2022.119086

Zhang Ying , Hou Xuhang . Node importance assessment method based on double deep Q-network [C ] // Proceedings of the 5th International Conference on Frontiers Technology of Information and Computer . Piscataway : IEEE , 2023 : 870 - 873 . DOI: 10.1109/icftic59930.2023.10456255 http://dx.doi.org/10.1109/icftic59930.2023.10456255

Rashid Y , Bhat J I . OlapGN: A multi-layered graph convolution network-based model for locating influential nodes in graph networks [J ] . Knowledge-Based Systems , 2024 , 283 : 111163 . DOI: 10.1016/j.knosys.2023.111163 http://dx.doi.org/10.1016/j.knosys.2023.111163

Lalou M , Tahraoui M A , Kheddouci H . The critical node detection problem in networks: A survey [J ] . Computer Science Review , 2018 , 28 : 92 - 117 . DOI: 10.1016/j.cosrev.2018.02.002 http://dx.doi.org/10.1016/j.cosrev.2018.02.002

Curado M , Tortosa L , Vicent J F . A novel measure to identify influential nodes: Return random walk gravity centrality [J ] . Information Sciences , 2023 , 628 : 177 - 195 . DOI: 10.1016/j.ins.2023.01.097 http://dx.doi.org/10.1016/j.ins.2023.01.097

Ullah A , Wang Bin , Sheng Jinfang , et al . Identifying vital nodes from local and global perspectives in complex networks [J ] . Expert Systems with Applications , 2021 , 186 : 115778 . DOI: 10.1016/j.eswa.2021.115778 http://dx.doi.org/10.1016/j.eswa.2021.115778

Zhang Haotian , Zhong Shen , Deng Yong , et al . LFIC: Identifying influential nodes in complex networks by local fuzzy information centrality [J ] . IEEE Transactions on Fuzzy Systems , 2022 , 30 ( 8 ): 3284 - 3296 . DOI: 10.1109/tfuzz.2021.3112226 http://dx.doi.org/10.1109/tfuzz.2021.3112226

Zhong Shen , Zhang Haotian , Deng Yong . Identification of influential nodes in complex networks: A local degree dimension approach [J ] . Information Sciences , 2022 , 610 : 994 - 1009 . DOI: 10.1016/j.ins.2022.07.172 http://dx.doi.org/10.1016/j.ins.2022.07.172

Yu Enyu , Wang Yueping , Fu Yan , et al . Identifying critical nodes in complex networks via graph convolutional networks [J ] . Knowledge-Based Systems , 2020 , 198 : 105893 . DOI: 10.1016/j.knosys.2020.105893 http://dx.doi.org/10.1016/j.knosys.2020.105893

Lv Laishui , Zhang Kun , Bardou D , et al . A new centrality measure based on random walks for multilayer networks under the framework of tensor computation [J ] . Physica A: Statistical Mechanics and its Applications , 2019 , 526 : 121000 . DOI: 10.1016/j.physa.2019.04.236 http://dx.doi.org/10.1016/j.physa.2019.04.236

Lv Laishui , Zhang Ting , Hu Peng , et al . An improved gravity centrality for finding important nodes in multi-layer networks based on multi-PageRank [J ] . Expert Systems with Applications , 2024 , 238 : 122171 . DOI: 10.1016/j.eswa.2023.122171 http://dx.doi.org/10.1016/j.eswa.2023.122171

Ni Chengzhang , Yang Jun , Pang Zezhao , et al . Seeding strategy based on weighted gravity centrality in multiplex networks [J ] . IEEE Transactions on Network Science and Engineering , 2023 , 10 ( 1 ): 331 - 345 . DOI: 10.1109/tnse.2022.3208343 http://dx.doi.org/10.1109/tnse.2022.3208343

Venkatakrishna Rao K , Katukuri M , Jagarapu M . CIM: Clique-based heuristic for finding influential nodes in multilayer networks [J ] . Applied Intelligence , 2022 , 52 ( 5 ): 5173 - 5184 . DOI: 10.1007/s10489-021-02656-0 http://dx.doi.org/10.1007/s10489-021-02656-0

Lv Laishui , Hu Peng , Zheng Zijun , et al . A community-based centrality measure for identifying key nodes in multilayer networks [J ] . IEEE Transactions on Computational Social Systems , 2024 , 11 ( 2 ): 2448 - 2463 . DOI: 10.1109/tcss.2023.3297902 http://dx.doi.org/10.1109/tcss.2023.3297902

梅汉涛 , 程光 , 朱怡霖 , 等 . Tor被动流量分析综述 [J ] . 软件学报 , 2025 , 36 ( 1 ): 253 - 288 .

Mei Hantao , Cheng Guang , Zhu Yilin , et al . Survey on Tor passive traffic analysis [J ] . Journal of Software , 2025 , 36 ( 1 ): 253 - 288 . (in Chinese)

Shen Meng , Wu Jinhe , Ai Junyu , et al . Swallow: A transfer-robust website fingerprinting attack via consistent feature learning [C ] // Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security . New York : ACM , 2025 : 1574 - 1588 . DOI: 10.1145/3719027.3744795 http://dx.doi.org/10.1145/3719027.3744795

Shen Meng , Wu Jinhe , Ye Ke , et al . Robust detection of malicious encrypted traffic via contrastive learning [J ] . IEEE Transactions on Information Forensics and Security , 2025 , 20 : 4228 - 4242 . DOI: 10.1109/TIFS.2025.3560560 http://dx.doi.org/10.1109/TIFS.2025.3560560

Qing Yuqi , Yin Qilei , Deng Xinhao , et al . Training robust classifiers for classifying encrypted traffic under dynamic network conditions [C ] // Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security . New York : ACM , 2025 : 3564 - 3578 . DOI: 10.1145/3719027.3765073 http://dx.doi.org/10.1145/3719027.3765073

Fu Chuanpu , Li Qi , Bertino E , et al . Training with only 1.0‰ samples: Malicious traffic detection via cross-modality feature fusion [C ] // Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security . New York : ACM , 2025 : 3930 - 3944 . DOI: 10.1145/3719027.3765143 http://dx.doi.org/10.1145/3719027.3765143

郑献春 , 王瑞 , 闫皓楠 , 等 . 基于分布式爬虫的高性能Tor网络内容监控系统 [J ] . 信息安全学报 , 2023 , 8 ( 1 ): 144 - 153 .

Zheng Xianchun , Wang Rui , Yan Haonan , et al . A high performance Tor web content monitoring system based on distributed crawlers [J ] . Journal of Cyber Security , 2023 , 8 ( 1 ): 144 - 153 . (in Chinese)

孙学良 , 黄安欣 , 罗夏朴 , 等 . 针对Tor的网页指纹识别研究综述 [J ] . 计算机研究与发展 , 2021 , 58 ( 8 ): 1773 - 1788 . DOI: 10.7544/issn1000-1239.2021.20200498 http://dx.doi.org/10.7544/issn1000-1239.2021.20200498

Sun Xueliang , Huang Anxin , Luo Xiaopu , et al . Webpage fingerprinting identification on Tor: A survey [J ] . Journal of Computer Research and Development , 2021 , 58 ( 8 ): 1773 - 1788 . (in Chinese) . DOI: 10.7544/issn1000-1239.2021.20200498 http://dx.doi.org/10.7544/issn1000-1239.2021.20200498

罗军舟 , 杨明 , 凌振 , 等 . 匿名通信与暗网研究综述 [J ] . 计算机研究与发展 , 2019 , 56 ( 1 ): 103 - 130 .

Luo Junzhou , Yang Ming , Ling Zhen , et al . Anonymous communication and darknet: A survey [J ] . Journal of Computer Research and Development , 2019 , 56 ( 1 ): 103 - 130 . (in Chinese)

马传旺 , 张宇 , 方滨兴 , 等 . 匿名网络综述 [J ] . 软件学报 , 2023 , 34 ( 1 ): 404 - 420 .

Ma Chuanwang , Zhang Yu , Fang Binxing , et al . Survey on anonymous networks [J ] . Journal of Software , 2023 , 34 ( 1 ): 404 - 420 . (in Chinese)

Zhang Wenzhen , Lu Tianbo , Du Zeyu . TNRAS: Tor nodes reliability analysis scheme [C ] // Proceedings of the 11th International Conference on Communication and Network Security . New York : ACM , 2021 : 21 - 26 . DOI: 10.1145/3507509.3507512 http://dx.doi.org/10.1145/3507509.3507512

Cui Jun , Huang Changqi , Meng Huan , et al . Tor network anonymity evaluation based on node anonymity [J ] . Cybersecurity , 2023 , 6 ( 1 ): 55 . DOI: 10.1186/s42400-023-00191-8 http://dx.doi.org/10.1186/s42400-023-00191-8

Zhai Jiangtao , Zhang Kaijie , Zeng Xiaolong , et al . FlowCorrGCN: Enhancing flow correlation through graph convolutional networks and triplet networks [J ] . International Journal of Intelligent Systems , 2024 , 2024 : 8823511 . DOI: 10.1155/2024/8823511 http://dx.doi.org/10.1155/2024/8823511

Qiu Qinjun , Liu Jiandong , Hao Mengqi , et al . DCKH-CNN: A multimetric graph-based convolutional neural network for identifying key influential nodes in earth surface data linked networks [J ] . Transactions in GIS , 2025 , 29 ( 2 ): e70016 . DOI: 10.1111/tgis.70016 http://dx.doi.org/10.1111/tgis.70016

刘琳岚 , 谭镇阳 , 舒坚 . 基于图神经网络的机会网络节点重要度评估方法 [J ] . 计算机研究与发展 , 2022 , 59 ( 4 ): 834 - 851 .

Liu Linlan , Tan Zhenyang , Shu Jian . Node importance estimation method for opportunistic network based on graph neural networks [J ] . Journal of Computer Research and Development , 2022 , 59 ( 4 ): 834 - 851 . (in Chinese)

Xiong You , Hu Zheng , Su Chang , et al . Vital node identification in complex networks based on autoencoder and graph neural network [J ] . Applied Soft Computing , 2024 , 163 : 111895 . DOI: 10.1016/j.asoc.2024.111895 http://dx.doi.org/10.1016/j.asoc.2024.111895

Yu Enyu , Fu Yan , Zhou Junlin , et al . Predicting critical nodes in temporal networks by dynamic graph convolutional networks [J ] . Applied Sciences , 2023 , 13 ( 12 ): 7272 . DOI: 10.3390/app13127272 http://dx.doi.org/10.3390/app13127272

Chan C Y , Ioannidis Y E . Bitmap index design and evaluation [C ] // Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data . New York : ACM , 1998 : 355 - 366 . DOI: 10.1145/276304.276336 http://dx.doi.org/10.1145/276304.276336

The Tor Project . Path selection and constraints [EB/OL ] . ( 2024-07-16 )[ 2024-12-10 ] . https://spec.torproject.org/path-spec/path-selection-constraints.html https://spec.torproject.org/path-spec/path-selection-constraints.html .

Edman M , Syverson P . As-awareness in tor path selection [C ] // Proceedings of the 16th ACM Conference on Computer and Communications Security . New York : ACM , 2009 : 380 - 389 . DOI: 10.1145/1653662.1653708 http://dx.doi.org/10.1145/1653662.1653708

Schlichtkrull M , Kipf T N , Bloem P , et al . Modeling relational data with graph convolutional networks [C ] // Proceedings of the 15th International Conference on the Semantic Web . Heidelberg : Springer , 2018 : 593 - 607 . DOI: 10.1007/978-3-319-93417-4_38 http://dx.doi.org/10.1007/978-3-319-93417-4_38

Wang Xiao , Ji Houye , Shi Chuan , et al . Heterogeneous graph attention network [C ] // The World Wide Web Conference . New York : ACM , 2019 : 2022 - 2032 . DOI: 10.1145/3308558.3313562 http://dx.doi.org/10.1145/3308558.3313562

Wang Chunmian , Luo Junzhou , Ling Zhen , et al . A comprehensive and long-term evaluation of tor V3 onion services [C ] // IEEE INFOCOM 2023 - IEEE Conference on Computer Communications . Piscataway : IEEE , 2023 : 1 - 10 . DOI: 10.1109/infocom53939.2023.10229057 http://dx.doi.org/10.1109/infocom53939.2023.10229057

Sendner C , Stang J , Dmitrienko A , et al . MirageFlow: A new bandwidth inflation attack on Tor [C ] // Proceedings of the 31st Annual Network and Distributed System Security Symposium . Rosten : Internet Society , 2024 . DOI: 10.14722/ndss.2024.241133 http://dx.doi.org/10.14722/ndss.2024.241133 .

Döpmann C , Tschorsch F . Modeling tor network growth by extrapolating consensus data [C ] // Proceedings of the 18th International Conference on Availability, Reliability and Security . New York : ACM , 2023 : 29 . DOI: 10.1145/3600160.3600164 http://dx.doi.org/10.1145/3600160.3600164

The Tor Project . Stem [EB/OL ] . ( 2013-03-26 ) [ 2024-12-10 ] . https://github.com/torproject/stem https://github.com/torproject/stem .

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