CSPM EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 4 (2025) Security and Safety, 4 (2025) 2024024 References
Issue
Security and Safety
Volume 4, 2025
Security and Safety in Network Simulation and Evaluation
Article Number 2024024
Number of page(s) 21
Section Information Network
DOI https://doi.org/10.1051/sands/2024024
Published online 05 March 2025
  1. Dingledine R, Mathewson N, Syverson PF, et al. Tor: The second-generation onion router. In: USENIX Security Symposium, vol. 4, 2004, 303–320. [Google Scholar]
  2. Inc., T.T.P. Tor Metrics Portal, https://metrics.torproject.org/, last accessed 4 Aug. 2024. [Google Scholar]
  3. Upadhyaya R and Jain A. Cyber ethics and cyber crime: A deep dwelved study into legality, ransomware, underground web and bitcoin wallet. In: 2016 International Conference on Computing, Communication and Automation (ICCCA), 2016, 143–148. [Google Scholar]
  4. Ghafir I, Svoboda J and Prenosil V. Tor-based malware and tor connection detection. In: International Conference on Frontiers of Communications, Networks and Applications (ICFCNA 2014 - Malaysia), 2014, 1–6. [Google Scholar]
  5. Salas Conde JJ, Martín Ortíz M and Carneiro Díaz VM. Methodology for identification and classifying of cybercrime on tor network through the use of cryptocurrencies based on web textual contents. Computación y Sistemas 2022; 26 347–356. [Google Scholar]
  6. Nurmi J. Understanding the usage of anonymous onion services: Empirical experiments to study criminal activities in the tor network, 2019. [Google Scholar]
  7. Jardine E. The dark web dilemma: Tor, anonymity and online policing. Global Commission on Internet Governance Paper Series, 2015. [Google Scholar]
  8. Saleem J, Islam R and Kabir MA. The anonymity of the dark web: A survey. IEEE Access 2022; 10 33628–33660. [CrossRef] [Google Scholar]
  9. Galteland H and Gjøsteen K. Adversaries monitoring tor traffic crossing their jurisdictional border and reconstructing tor circuits. arXiv preprint arXiv:1808.09237, 2018. [Google Scholar]
  10. Nasr M, Bahramali A and Houmansadr A. Deepcorr: Strong flow correlation attacks on tor using deep learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018, 1962–1976. [Google Scholar]
  11. Li J, Gu C, Zhang X, et al. Attcorr: A novel deep learning model for flow correlation attacks on tor. In: 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), IEEE, 2021, 427–430. [Google Scholar]
  12. Oh SE, Yang T, Mathews N, et al. Deepcoffea: Improved flow correlation attacks on tor via metric learning and amplification. In: 2022 IEEE Symposium on Security and Privacy (SP), IEEE, 2022, 1915–1932. [Google Scholar]
  13. Guan Z, Liu C, Xiong G, et al. Flowtracker: Improved flow correlation attacks with denoising and contrastive learning. Comput Secur 2023; 125 103018. [CrossRef] [Google Scholar]
  14. Lopes D, Dong JD, Medeiros P, et al. Flow correlation attacks on tor onion service sessions with sliding subset sum, 2024. [Google Scholar]
  15. Wang M, Li Y, Wang X, et al. 2ch-TCN: A website fingerprinting attack over tor using 2-channel temporal convolutional networks. In: 2020 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2020, 1–7. [Google Scholar]
  16. Bang J, Jeong J and Lee J. Fedfingerprinting: A federated learning approach to website fingerprinting attacks in tor networks. IEEE Access 2023. [Google Scholar]
  17. Song C, Fan Z, Wang H, et al. Seamless website fingerprinting in multiple environments. arXiv preprint arXiv:2407.19365, 2024. [Google Scholar]
  18. Chen M, Wang X, Shi J, et al. Napping guard: Deanonymizing tor hidden service in a stealthy way. In: 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, 2020, 699–706. [Google Scholar]
  19. Li T, Liu K, Feng W, et al. Heterotic: A robust network flow watermarking based on heterogeneous time channels. Comput Netw 2022; 219 109424. [CrossRef] [Google Scholar]
  20. Zhang Q, Teng Z, Wang X, et al. Hsdirsniper: A new attack exploiting vulnerabilities in tor’s hidden service directories. In: Proceedings of the ACM on Web Conference 2024, 2024, 1812–1823. [Google Scholar]
  21. Shirazi F, Goehring M and Diaz C. Tor experimentation tools. In: 2015 IEEE Security and Privacy Workshops, IEEE, 2015, 206–213. [Google Scholar]
  22. Wang Q and Cao W. A tor anonymity attack experiment platform driven by Raspberry Pi. In: 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan), IEEE 2020, 569–574. [Google Scholar]
  23. Anderson C. Docker [software engineering]. IEEE Softw 2015; 32: 102–c3. [Google Scholar]
  24. Google: K8s: Production-grade container orchestration, https://kubernetes.io/, last accessed 4 Aug. 2023. [Google Scholar]
  25. Project T. Tor Browser, https://www.torproject.org/download/, last accessed 10 June 2023. [Google Scholar]
  26. Mathewson, N. Tor Rendezvous Specification, https://gitlab.torproject.org/tpo/core/torspec/-/blob/HEAD/rend-spec-v3.txt, last accessed 12 June 2023. [Google Scholar]
  27. William P, Jawale M, Pawar A, et al. Systematic approach for detection and assessment of dark web threat evolution. In: Using Computational Intelligence for the Dark Web and Illicit Behavior Detection, IGI Global, 2022, 230–256. [Google Scholar]
  28. Karunanayake I, Ahmed N, Malaney R, et al. De-anonymisation attacks on tor: A survey. IEEE Commun Surveys Tutor 2021; 23 2324–2350. [CrossRef] [Google Scholar]
  29. Guan Z, Xiong G, Li Z, et al. Restor: A pre-processing model for removing the noise pattern in flow correlation. In: 2020 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2020, 1–6. [Google Scholar]
  30. Tian J, Gou G, Guan Y, et al. Universal perturbation for flow correlation attack on tor. In: 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), IEEE, 2021, 1–9. [Google Scholar]
  31. Hafeez MA, Ali Y, Han KH, et al. GPU-accelerated deep learning-based correlation attack on tor networks. IEEE Access 2023. [Google Scholar]
  32. Guan Z, Liu C, Gou G, et al. A blind flow fingerprinting and correlation method against disturbed anonymous traffic based on pattern reconstruction. Comput Netw 2024; 254 110831. [CrossRef] [Google Scholar]
  33. Schroff F, Kalenichenko D and Philbin J. Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 815–823. [Google Scholar]
  34. Sirinam P, Imani M, Juarez M, et al. Deep fingerprinting: Undermining website fingerprinting defenses with deep learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018, 1928–1943. [Google Scholar]
  35. Cherubin G, Jansen R and Troncoso C. Online website fingerprinting: Evaluating website fingerprinting attacks on tor in the real world. In: 31st USENIX Security Symposium (USENIX Security 22), 2022, 753–770. [Google Scholar]
  36. Kwon A, AlSabah M, Lazar D, et al. Circuit fingerprinting attacks: Passive deanonymization of tor hidden services. In: 24th USENIX Security Symposium (USENIX Security 15), 2015, 287–302. [Google Scholar]
  37. Hayes J and Danezis G. k-fingerprinting: A robust scalable website fingerprinting technique. In: 25th USENIX Security Symposium (USENIX Security 16), 2016, 1187–1203. [Google Scholar]
  38. Panchenko A, Mitseva A, Henze M, et al. Analysis of fingerprinting techniques for tor hidden services. In: Proceedings of the 2017 on Workshop on Privacy in the Electronic Society, 2017, 165–175. [Google Scholar]
  39. Henri S, Garcia-Aviles G, Serrano P, et al. Protecting against website fingerprinting with multihoming. Proc Priv Enhancing Technol 2020. [Google Scholar]
  40. Xu Y, Wang L, Li J, et al. Zoomer: A website fingerprinting attack against tor hidden services. In: International Conference on Information and Communications Security, Springer, 2023, 370–382. [Google Scholar]
  41. Wang M, Chen M, Li Z, et al. Deanonymize tor hidden services using remote website fingerprinting. In: 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, 2023, 998–1005. [Google Scholar]
  42. Wang X, Chen S and Jajodia S. Network flow watermarking attack on low-latency anonymous communication systems. In: 2007 IEEE Symposium on Security and Privacy (SP’07), IEEE, 2007, 116–130. [Google Scholar]
  43. Ling Z, Luo J, Xu D, et al. Novel and practical sdn-based traceback technique for malicious traffic over anonymous networks. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, IEEE, 2019, 1180–1188. [Google Scholar]
  44. Chen M, Wang X, Liu T, et al. Signalcookie: Discovering guard relays of hidden services in parallel. In: 2019 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2019, 1–7. [Google Scholar]
  45. Iacovazzi A, Frassinelli D and Elovici Y. The {DUSTER} attack: Tor onion service attribution based on flow watermarking with track hiding. In: 22nd International Symposium on Research in Attacks, Intrusions and Defenses, (RAID 2019), 2019, 213–225. [Google Scholar]
  46. Zhang Q, Chen M, Wang X, et al. Knock-knock: De-anonymise hidden services by exploiting service answer vulnerability. In: 2024 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, 2024, 1–7. [Google Scholar]
  47. Project T. How Often Does Tor Change its Paths? https://support.torproject.org/about/change-paths/, last accessed 15 June 2023. [Google Scholar]
  48. O’Gorman G and Blott S. Simulating low-latency anonymous networks. In: Proceedings of the 2009 Spring Simulation Multiconference, 2009, 1–8. [Google Scholar]
  49. Doswell S, Aslam N, Kendall D, et al. The novel use of bridge relays to provide persistent tor connections for mobile devices. In: 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), IEEE, 2013, 3371–3375. [Google Scholar]
  50. Musa A and Awan I. Functional and performance analysis of discrete event network simulation tools. Simul Model Practice Theory 2022; 116 102470. [CrossRef] [Google Scholar]
  51. Jansen R and Hopper NJ. Shadow: Running tor in a box for accurate and efficient experimentation, 2011. [Google Scholar]
  52. Bauer K, Sherr M and Grunwald D. {ExperimenTor}: A testbed for safe and realistic tor experimentation. In: 4th Workshop on Cyber Security Experimentation and Test (CSET 11) 2011. [Google Scholar]
  53. Vahdat A, Yocum K, Walsh K, et al. Scalability and accuracy in a large-scale network emulator. ACM SIGOPS Operating Syst Rev 2002; 36 271–284. [CrossRef] [Google Scholar]
  54. Wacek C, Tan H, Bauer KS, et al. An empirical evaluation of relay selection in tor. In: NDSS, 2013. [Google Scholar]
  55. AlSabah M, Bauer K, Elahi T, et al. The path less travelled: Overcoming tor’s bottlenecks with traffic splitting. In: Privacy Enhancing Technologies: 13th International Symposium, PETS 2013, Bloomington, IN, USA, July 10-12, 2013. Proceedings 13, Springer, 2013, 143–163. [Google Scholar]
  56. Elahi T, Bauer K, AlSabah M, et al. Changing of the guards: A framework for understanding and improving entry guard selection in tor. In: Proceedings of the 2012 ACM Workshop on Privacy in the Electronic Society, 2012, 43–54. [Google Scholar]
  57. Johnson A, Wacek C, Jansen R, et al. Users get routed: Traffic correlation on tor by realistic adversaries. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, 2013, 337–348. [Google Scholar]
  58. Singh S. Large-scale emulation of anonymous communication networks. Master’s Thesis, University of Waterloo, 2014. [Google Scholar]
  59. Jansen R, Geddes J, Wacek C, et al. Never been {KIST}: {Tor’s} congestion management blossoms with {Kernel-Informed} socket transport. In: 23rd USENIX Security Symposium (USENIX Security 14), 2014, 127–142. [Google Scholar]
  60. Jansen R, Tracey J and Goldberg I. Once is never enough: Foundations for sound statistical inference in tor network experimentation. In: 30th USENIX Security Symposium (USENIX Security 21), 2021, 3415–3432. [Google Scholar]
  61. Jansen R, Newsome J and Wails R. Co-opting linux processes for {High-Performance} network simulation. In: 2022 USENIX Annual Technical Conference (USENIX ATC 22), 2022, 327–350. [Google Scholar]
  62. Cheng CY. High-fidelity simulation of load balancing methods in tor. Ph.D. Thesis, University of Illinois at Urbana-Champaign, 2020. [Google Scholar]
  63. Chun B, Culler D, Roscoe T, et al. Planetlab: An overlay testbed for broad-coverage services. ACM SIGCOMM Comput Commun Rev 2003; 33: 3–12. [CrossRef] [Google Scholar]
  64. Bauer K, McCoy D, Grunwald D, et al. Low-resource routing attacks against tor. In: Proceedings of the 2007 ACM workshop on Privacy in Electronic Society, 2007, 11–20. [Google Scholar]
  65. Akhoondi M, Yu C and Madhyastha HV. Lastor: A low-latency as-aware tor client. In: 2012 IEEE Symposium on Security and Privacy, 2012, IEEE, 476–490. [Google Scholar]
  66. Benzel T. The science of cyber security experimentation: The deter project. In: Proceedings of the 27th Annual Computer Security Applications Conference, 2011, 137–148. [Google Scholar]
  67. Mirkovic J, Benzel TV, Faber T, et al. The deter project: Advancing the science of cyber security experimentation and test. In: 2010 IEEE International Conference on Technologies for Homeland Security (HST), IEEE, 2010, 1–7. [Google Scholar]
  68. Chakravarty S, Stavrou A and Keromytis AD. Traffic analysis against low-latency anonymity networks using available bandwidth estimation. In: European Symposium on Research in Computer Security, Springer, 2010, 249–267. [Google Scholar]
  69. White B, Lepreau J, Stoller L, et al. An integrated experimental environment for distributed systems and networks. ACM SIGOPS Operating Syst Rev 2002; 36 255–270. [CrossRef] [Google Scholar]
  70. Das A and Borisov N. Securing anonymous communication channels under the selective dos attack. In: Financial Cryptography and Data Security: 17th International Conference, FC 2013, Okinawa, Japan, April 1-5, 2013, Revised Selected Papers 17, Springer, 2013, 362–370. [Google Scholar]
  71. Wang P, Liu H, Wang B, et al. Simulation of dark network scene based on the big data environment. In: Proceedings of the International Conference on Information Technology and Electrical Engineering, 2018, 1–6. [Google Scholar]
  72. Zheng X, Yan H, Wang R, et al. Lunar: A practical anonymous network simulation platform. Secur Commun Netw 2022; 2022: 5832124. [Google Scholar]
  73. Hat R: Understanding Virtualization, https://www.redhat.com/en/topics/virtualization, last accessed 15 July 2023. [Google Scholar]
  74. dockercon: Dockerfile Reference, https://docs.docker.com/engine/reference/builder/, last accessed 15 Sep. 2023. [Google Scholar]
  75. Project T: Tor-stable Manual, https://2019.www.torproject.org/docs/tor-manual.html.en, last accessed 17 Sep. 2023. [Google Scholar]
  76. feiskyer: kubeadm, https://kubernetes.feisky.xyz/setup/cluster/kubeadm, last accessed 18 Sep. 2023. [Google Scholar]
  77. rbrtbnfgl: flannel, https://github.com/flannel-io/flannel, last accessed 19 Sep. 2023. [Google Scholar]
  78. Mathewson N. Tor’s Protocol Specifications, https://gitweb.torproject.org/torspec.git/tree/tor-spec.txt, last accessed 12 June 2023. [Google Scholar]
  79. Docs S. Api, https://stem.torproject.org/api.html, last accessed 18 Sep. 2023. [Google Scholar]
  80. Sedgwick P. Pearson’s Correlation Coefficient. BMJ, 2012. [PubMed] [Google Scholar]
  81. Debian: Installing Debian GNU/Linux from a Unix/Linux System, https://www.debian.org/releases/stable/amd64/apds03.en.html, last accessed 10 June 2024. [Google Scholar]
  82. Pi R. Raspberry Pi, https://www.raspberrypi.org/products/, last accessed 10 June 2024. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.