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Spiders like Onions: on the Network of Tor Hidden Services

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Published:13 May 2019Publication History

ABSTRACT

Tor hidden services allow offering and accessing various Internet resources while guaranteeing a high degree of provider and user anonymity. So far, most research work on the Tor network aimed at discovering protocol vulnerabilities to de-anonymize users and services. Other work aimed at estimating the number of available hidden services and classifying them. Something that still remains largely unknown is the structure of the graph defined by the network of Tor services. In this paper, we describe the topology of the Tor graph (aggregated at the hidden service level) measuring both global and local properties by means of well-known metrics. We consider three different snapshots obtained by extensively crawling Tor three times over a 5 months time frame. We separately study these three graphs and their shared “stable” core. In doing so, other than assessing the renowned volatility of Tor hidden services, we make it possible to distinguish time dependent and structural aspects of the Tor graph. Our findings show that, among other things, the graph of Tor hidden services presents some of the characteristics of social and surface web graphs, along with a few unique peculiarities, such as a very high percentage of nodes having no outbound links.

References

  1. Dimitris Achlioptas, Aaron Clauset, David Kempe, and Cristopher Moore. 2009. On the Bias of Traceroute Sampling: Or, Power-law Degree Distributions in Regular Graphs. J. ACM56, 4, Article 21 (July 2009), 28 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Re´ka Albert, Hawoong Jeong, and Albert-László Barabási. 1999. Internet: Diameter of the world-wide web. nature401, 6749 (1999), 130.Google ScholarGoogle Scholar
  3. Jeff Alstott, Ed Bullmore, and Dietmar Plenz. 2014. Powerlaw: a Python package for analysis of heavy-tailed distributions. PloS one9, 1 (2014), e85777.Google ScholarGoogle Scholar
  4. Robert Annessi and Martin Schmiedecker. 2016. NavigaTor: Finding Faster Paths to Anonymity. In IEEE European Symposium on Security and Privacy (Euro S&P). IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  5. Albert-László Barabási and Re´ka Albert. 1999. Emergence of Scaling in Random Networks. Science286, 5439 (1999), 509-512.Google ScholarGoogle Scholar
  6. Massimo Bernaschi, Alessandro Celestini, Stefano Guarino, and Flavio Lombardi. 2017. Exploring and Analyzing the Tor Hidden Services Graph. ACM Trans. Web11, 4, Article 24 (jul 2017), 24:1-24:26 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Alex Biryukov, Ivan Pustogarov, Fabrice Thill, and Ralf-Philipp Weinmann. 2014. Content and Popularity Analysis of Tor Hidden Services. In Distributed Computing Systems Workshops (ICDCSW), 2014 IEEE 34th International Conference on. 188-193. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Alex Biryukov, Ivan Pustogarov, and Ralf-Philipp Weinmann. 2013. Trawling for Tor Hidden Services: Detection, Measurement, Deanonymization. In Proceedings of the 2013 IEEE Symposium on Security and Privacy(SP '13). IEEE Computer Society, Washington, DC, USA, 80-94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Paolo Boldi, Andrea Marino, Massimo Santini, and Sebastiano Vigna. 2014. BUbiNG: Massive crawling for the masses. In Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion. 227-228. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Anthony Bonato. 2005. A Survey of Models of the Web Graph. In Combinatorial and Algorithmic Aspects of Networking, Alejandro Lopez-Ortiz and Angelem Hamel (Eds.). Lecture Notes in Computer Science, Vol. 3405. Springer Berlin Heidelberg, 159-172. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Stephen P. Borgatti and Martin G. Everett. 2006. A Graph-theoretic perspective on centrality. Social Networks28, 4 (2006), 466 - 484.Google ScholarGoogle Scholar
  12. Andrei Broder, Ravi Kumar, Farzin Maghoul, Prabhakar Raghavan, Sridhar Rajagopalan, Raymie Stata, Andrew Tomkins, and Janet Wiener. 2000. Graph structure in the Web. Computer Networks33, 1-6 (2000), 309 - 320. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Duncan S. Callaway, M. E. J. Newman, Steven H. Strogatz, and Duncan J. Watts. 2000. Network Robustness and Fragility: Percolation on Random Graphs. Phys. Rev. Lett.85 (Dec 2000), 5468-5471. Issue 25.Google ScholarGoogle ScholarCross RefCross Ref
  14. Alessandro Celestini and Stefano Guarino. 2017. Design, Implementation and Test of a Flexible Tor-oriented Web Mining Toolkit. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics(WIMS '17). ACM, New York, NY, USA, Article 19, 10 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Nicolas Christin. 2013. Traveling the Silk Road: A Measurement Analysis of a Large Anonymous Online Marketplace. In Proceedings of the 22Nd International Conference on World Wide Web(WWW '13). ACM, New York, NY, USA, 213-224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Aaron Clauset, Cosma Rohilla Shalizi, and Mark EJ Newman. 2009. Power-law distributions in empirical data. SIAM review51, 4 (2009), 661-703. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Manlio De Domenico and Alex Arenas. 2017. Modeling structure and resilience of the dark network. Phys. Rev. E95 (Feb 2017), 022313. Issue 2.Google ScholarGoogle Scholar
  18. Debora Donato, Stefano Leonardi, Stefano Millozzi, and Panayiotis Tsaparas. 2008. Mining the inner structure of the Web graph. Journal of Physics A: Mathematical and Theoretical41, 22(2008), 224017. http://stacks.iop.org/1751-8121/41/i=22/a=224017Google ScholarGoogle ScholarCross RefCross Ref
  19. Paul Erdos and Alfre´d Re´nyi. 1959. On random graphs. Publicationes Mathematicae Debrecen6 (1959), 290-297.Google ScholarGoogle Scholar
  20. P. Erdos and A Re´nyi. 1960. On the Evolution of Random Graphs. In PUBLICATION OF THE MATHEMATICAL INSTITUTE OF THE HUNGARIAN ACADEMY OF SCIENCES. 17-61.Google ScholarGoogle Scholar
  21. Massimo Franceschet. 2011. PageRank: Standing on the Shoulders of Giants. Commun. ACM54, 6 (June 2011), 92-101. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Shalini Ghosh, Ariyam Das, Phil Porras, Vinod Yegneswaran, and Ashish Gehani. 2017. Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD '17). ACM, New York, NY, USA, 1793-1802. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Virgil Griffith, Yang Xu, and Carlo Ratti. 2017. Graph Theoretic Properties of the Darkweb. arXiv preprint arXiv:1704.07525(2017).Google ScholarGoogle Scholar
  24. Rob Jansen, Kevin Bauer, Nicholas Hopper, and Roger Dingledine. 2012. Methodically Modeling the Tor Network. In Proceedings of the 5th USENIX Conference on Cyber Security Experimentation and Test(CSET'12). USENIX Association, Berkeley, CA, USA, 8-8. http://dl.acm.org/citation.cfm?id=2372336.2372347 Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Rohit Khare, Doug Cutting, Kragen Sitaker, and Adam Rifkin. 2004. Nutch: A flexible and scalable open-source web search engine. Oregon State University1 (2004), 32-32.Google ScholarGoogle Scholar
  26. JonM. Kleinberg, Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, and AndrewS. Tomkins. 1999. The Web as a Graph: Measurements, Models, and Methods. In Computing and Combinatorics, Takano Asano, Hideki Imai, D.T. Lee, Shin-ichi Nakano, and Takeshi Tokuyama (Eds.). Lecture Notes in Computer Science, Vol. 1627. Springer Berlin Heidelberg, 1-17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Ravi Kumar, Jasmine Novak, and Andrew Tomkins. 2010. Structure and Evolution of Online Social Networks. Springer New York, New York, NY, 337-357.Google ScholarGoogle Scholar
  28. Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, D Sivakumar, Andrew Tomkins, and Eli Upfal. 2000. Stochastic models for the Web graph. In Foundations of Computer Science, 2000. Proceedings. 41st Annual Symposium on. 57-65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Oliver Lehmberg, Robert Meusel, and Christian Bizer. 2014. Graph Structure in the Web: Aggregated by Pay-level Domain. In Proceedings of the 2014 ACM Conference on Web Science(WebSci '14). ACM, New York, NY, USA, 119-128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Robert Meusel, Sebastiano Vigna, Oliver Lehmberg, and Christian Bizer. 2015. The Graph Structure in the Web - Analyzed on Different Aggregation Levels. The Journal of Web Science1, 1 (2015), 33-47.Google ScholarGoogle ScholarCross RefCross Ref
  31. Gordon Mohr, Michael Stack, Igor Ranitovic, Dan Avery, and Michele Kimpton. 2004. An Introduction to Heritrix An open source archival quality web crawler. In In IWAW'04, 4th International Web Archiving Workshop. Citeseer.Google ScholarGoogle Scholar
  32. Mark Newman, Albert-Laszlo Barabasi, and Duncan J. Watts. 2006. The Structure and Dynamics of Networks: (Princeton Studies in Complexity). Princeton University Press, Princeton, NJ, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. M. E. J. Newman. 2003. Mixing patterns in networks. Phys. Rev. E67, 2 (Feb 2003), 026126.Google ScholarGoogle Scholar
  34. Gareth Owen and Nick Savage. 2015. The Tor dark net. (2015).Google ScholarGoogle Scholar
  35. Gareth Owen and Nick Savage. 2016. Empirical analysis of Tor hidden services. IET Information Security10, 3 (2016), 113-118.Google ScholarGoogle Scholar
  36. Iskander Sanchez-Rola, Davide Balzarotti, and Igor Santos. 2017. The Onions Have Eyes: A Comprehensive Structure and Privacy Analysis of Tor Hidden Services. In Proceedings of the 26th International Conference on World Wide Web(WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1251-1260. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. M. Ángeles Serrano, Ana Maguitman, Marián Bogu&ntiled;á, Santo Fortunato, and Alessandro Vespignani. 2007. Decoding the Structure of the WWW: A Comparative Analysis of Web Crawls. ACM Trans. Web1, 2, Article 10 (Aug. 2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Martijn Spitters, Stefan Verbruggen, and Mark van Staalduinen. 2014. Towards a Comprehensive Insight into the Thematic Organization of the Tor Hidden Services. In Intelligence and Security Informatics Conference (JISIC), 2014 IEEE Joint. 220-223. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Sebastiano Vigna. 2013. Fibonacci binning. arXiv preprint arXiv:1312.3749(2013).Google ScholarGoogle Scholar
  40. Duncan J. Watts. 1999. Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton University Press, Princeton, NJ, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Other conferences
    WWW '19: The World Wide Web Conference
    May 2019
    3620 pages
    ISBN:9781450366748
    DOI:10.1145/3308558

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    Publication History

    • Published: 13 May 2019

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