Advertisement

DigesTor: Comparing Passive Traffic Analysis Attacks on Tor

  • Katharina Kohls
  • Christina Pöpper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11098)

Abstract

The Tor anonymity network represents a rewarding target for de-anonymization attacks, in particular by large organizations and governments. Tor is vulnerable to confirmation attacks, in which powerful adversaries compromise user anonymity by correlating transmissions between entry and exit nodes. As the experimental evaluation of such attacks is challenging, a fair comparison of passive traffic analysis techniques is hardly possible. In this work, we provide a first comparative evaluation of confirmation attacks and assess their impact on the real world. For this purpose, we release DigesTor, an analysis framework that delivers a foundation for comparability to support future research in this context. The framework runs a virtual private Tor network to generate traffic for representative scenarios, on which arbitrary attacks can be evaluated. Our results show the effects of recent and novel attack techniques and we demonstrate the capabilities of DigesTor using the example of mixing as a countermeasure against traffic analysis attacks.

Keywords

Tor Traffic analysis Confirmation attack Mixing 

Notes

Acknowledgments

This work was supported in part by Intel (ICRI-CARS) and the German Research Foundation (DFG) Research Training Group GRK 1817/1. We would like to thank Maximilian Golla for his support with the experimental setup.

References

  1. 1.
    Biryukov, A., Pustogarov, I., Weinmann, R.-P.: Trawling for Tor hidden services: detection, measurement, deanonymization. In: Symposium on Security and Privacy, pp. 80–94. IEEE (2013)Google Scholar
  2. 2.
    Chakravarty, S., Barbera, M.V., Portokalidis, G., Polychronakis, M., Keromytis, A.D.: On the effectiveness of traffic analysis against anonymity networks using flow records. In: Faloutsos, M., Kuzmanovic, A. (eds.) PAM 2014. LNCS, vol. 8362, pp. 247–257. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-04918-2_24CrossRefGoogle Scholar
  3. 3.
    Danezis, G.: Statistical disclosure attacks. In: Gritzalis, D., De Capitani di Vimercati, S., Samarati, P., Katsikas, S. (eds.) SEC 2003. ITIFIP, vol. 122, pp. 421–426. Springer, Boston, MA (2003).  https://doi.org/10.1007/978-0-387-35691-4_40CrossRefGoogle Scholar
  4. 4.
    Danezis, G., Diaz, C., Troncoso, C.: Two-sided statistical disclosure attack. In: Borisov, N., Golle, P. (eds.) PET 2007. LNCS, vol. 4776, pp. 30–44. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75551-7_3CrossRefGoogle Scholar
  5. 5.
    Diaz, C., Preneel, B.: Taxonomy of mixes and dummy traffic. In: Deswarte, Y., Cuppens, F., Jajodia, S., Wang, L. (eds.) SEC 2004. IIFIP, vol. 148, pp. 217–232. Springer, Boston, MA (2004).  https://doi.org/10.1007/1-4020-8145-6_18CrossRefGoogle Scholar
  6. 6.
    Fu, X., Ling, Z., Luo, J., Yu, W., Jia, W., Zhao, W.: One cell is enough to break Tor’s anonymity. In: Proceedings of Black Hat Technical Security Conference, pp. 578–589 (2009)Google Scholar
  7. 7.
    Houmansadr, A., Borisov, N.: SWIRL: a scalable watermark to detect correlated network flows. In: NDSS (2011)Google Scholar
  8. 8.
    Houmansadr, A., Borisov, N.: The need for flow fingerprints to link correlated network flows. In: De Cristofaro, E., Wright, M. (eds.) PETS 2013. LNCS, vol. 7981, pp. 205–224. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-39077-7_11CrossRefGoogle Scholar
  9. 9.
    Houmansadr, A., Brubaker, C., Shmatikov, V.: The parrot is dead: observing unobservable network communications. In: Symposium on Security and Privacy, pp. 65–79. IEEE (2013)Google Scholar
  10. 10.
    icons8. Figure Icons. https://icons8.com. Accessed 23 Apr 2018
  11. 11.
    Jansen, R., Hopper, N.: Shadow: running Tor in a box for accurate and efficient experimentation. In: Symposium on Network and Distributed System Security, ser. NDSS 2012. Internet Society, San Diego, February 2012Google Scholar
  12. 12.
    Jansen, R., Johnson, A.: Safely measuring Tor. In: Conference on Computer and Communications Security, pp. 1553–1567. ACM (2016)Google Scholar
  13. 13.
    Kedogan, D., Agrawal, D., Penz, S.: Limits of anonymity in open environments. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 53–69. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-36415-3_4CrossRefGoogle Scholar
  14. 14.
    Kwon, A., AlSabah, M., Lazar, D., Dacier, M., Devadas, S.: Circuit fingerprinting attacks: passive deanonymization of tor hidden services. In: USENIX Security Symposium (2015)Google Scholar
  15. 15.
    Levine, B.N., Reiter, M.K., Wang, C., Wright, M.: Timing attacks in low-latency mix systems. In: Juels, A. (ed.) FC 2004. LNCS, vol. 3110, pp. 251–265. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-27809-2_25CrossRefGoogle Scholar
  16. 16.
    Ling, Z., Fu, X., Jia, W., Yu, W., Xuan, D., Luo, J.: Novel packet size-based covert channel attacks against anonymizer. IEEE Trans. Comput. 62(12), 2411–2426 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Ling, Z., Luo, J., Yu, W., Fu, X., Xuan, D., Jia, W.: A new cell counter based attack against Tor. In: Conference on Computer and Communications Security, pp. 578–589. ACM (2009)Google Scholar
  18. 18.
    Mathewson, N., Dingledine, R.: Practical traffic analysis: extending and resisting statistical disclosure. In: Martin, D., Serjantov, A. (eds.) PET 2004. LNCS, vol. 3424, pp. 17–34. Springer, Heidelberg (2005).  https://doi.org/10.1007/11423409_2CrossRefGoogle Scholar
  19. 19.
    Mittal, P., Khurshid, A., Juen, J., Caesar, M., Borisov, N.: Stealthy traffic analysis of low-latency anonymous communication using throughput fingerprinting. In: Conference on Computer and Communications Security, ser. CCS 2011, pp. 215–226. ACM, Chicago, October 2011Google Scholar
  20. 20.
    Murdoch, S.J., Danezis, G.: Low-cost traffic analysis of Tor. In: Symposium on Security and Privacy, ser. SP 2005, pp. 183–195. IEEE, Oakland, May 2005Google Scholar
  21. 21.
    Murdoch, S.J., Zieliński, P.: Sampled traffic analysis by internet-exchange-level adversaries. In: Borisov, N., Golle, P. (eds.) PET 2007. LNCS, vol. 4776, pp. 167–183. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75551-7_11CrossRefGoogle Scholar
  22. 22.
    Nithyanand, R., Starov, O., Zair, A., Gill, P., Schapira, M.: Measuring and mitigating as-level adversaries against Tor. In: Symposium on Network and Distributed System Security, ser. NDSS 2016. Internet Society, San Diego, February 2016Google Scholar
  23. 23.
    O’Connor, L.: On blending attacks for mixes with memory. In: Barni, M., Herrera-Joancomartí, J., Katzenbeisser, S., Pérez-González, F. (eds.) IH 2005. LNCS, vol. 3727, pp. 39–52. Springer, Heidelberg (2005).  https://doi.org/10.1007/11558859_4CrossRefGoogle Scholar
  24. 24.
    Sengar, H., Ren, Z., Wang, H., Wijesekera, D., Jajodia, S.: Tracking Skype VoIP calls over the internet. in International Conference on Computer Communications, pp. 1–5. IEEE (2010)Google Scholar
  25. 25.
    Serjantov, A., Dingledine, R., Syverson, P.: From a trickle to a flood: active attacks on several mix types. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 36–52. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-36415-3_3CrossRefGoogle Scholar
  26. 26.
    Shmatikov, V., Wang, M.-H.: Timing analysis in low-latency mix networks: attacks and defenses. In: Gollmann, D., Meier, J., Sabelfeld, A. (eds.) ESORICS 2006. LNCS, vol. 4189, pp. 18–33. Springer, Heidelberg (2006).  https://doi.org/10.1007/11863908_2CrossRefGoogle Scholar
  27. 27.
    Sun, Y., et al.: RAPTOR: routing attacks on privacy in Tor. In: USENIX Security Symposium, ser. USENIX 2016, pp. 271–286. USENIX, Washington, D.C., August 2015Google Scholar
  28. 28.
    The Tor Project. The Onion Router. https://www.torproject.org. Accessed 23 Apr 2018
  29. 29.
    The Tor Project. Tor Metrics. https://metrics.torproject.org. Accessed 23 Apr 2018
  30. 30.
    The Tor Project. Tor Security Advisory: “Relay Early” Traffic Confirmation Attack, July 2014. https://blog.torproject.org/blog/tor-security-advisory-relay-early-traffic-confirmation-attack. Accessed 23 Apr 2018
  31. 31.
    The Tor Project. Ethical Tor Research: Guidelines, November 2015. https://blog.torproject.org/blog/ethical-tor-research-guidelines. Accessed 23 Apr 2018
  32. 32.
    Wang, X., Chen, S., Jajodia, S.: Network flow watermarking attack on low-latency anonymous communication systems. In: Symposium on Security and Privacy, pp. 116–130. IEEE (2007)Google Scholar
  33. 33.
    Wang, X., Reeves, D.S.: Robust correlation of encrypted attack traffic through stepping stones by manipulation of interpacket delays. In: Conference on Computer and Communications Security. ACM, pp. 20–29 (2003)Google Scholar
  34. 34.
    Yu, W., Fu, X., Graham, S., Xuan, D., Zhao, W.: DSSS-based flow marking technique for invisible traceback. In: Symposium on Security and Privacy. IEEE, pp. 18–32 (2007)Google Scholar
  35. 35.
    Zhu, Y., Fu, X., Graham, B., Bettati, R., Zhao, W.: On flow correlation attacks and countermeasures in mix networks. In: Martin, D., Serjantov, A. (eds.) PET 2004. LNCS, vol. 3424, pp. 207–225. Springer, Heidelberg (2005).  https://doi.org/10.1007/11423409_13CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Ruhr-University BochumBochumGermany
  2. 2.New York University Abu DhabiAbu DhabiUnited Arab Emirates

Personalised recommendations