1. Network Architectures and Services, Georg Carle Faculty of Informatics Technische Universität München, Germany Privacy-Implications of Performance-Based Peer Selection by Onion-Routers: A Real-World Case Study using I2P Michael Herrmann Christian Grothoff Waterloo July 29 th, 2011

2. 2 Agenda ● Motivation ● Background I2P ● Attack ● Attack Data ● Deanonymization Data ● Summery/Recommendations

3. 3 Why Attack I2P? ● I2P is an anonymizing P2P network ● Unique features include: ● Uni-directional tunnels ● Performance-based peer selection ● Attacks based on these features give insights into their security implications

4. 4 Contribution of this Work ● We developed an attack on I2P version 0.83 ● Use a Denial-of-Service attack to facilitate traffic analysis ● Deanonymization targets are I2P Eepsites

5. 5 What is I2P? ● The Invisible Internet Project ● Multi-application framework for anonymous P2P networking ● Common usage is accessing internal services

6. 6 Eepsites ● Anybody can anonymously host a website in the I2P network

7. 7 Eepsites ● Anybody can anonymously host a website in the I2P network

8. 8 I2P Tunnels ● Uni-directional ● Tier-based peer selection ● Variable length: [0, 5]

9. 9 Tier-Based Peer Selection ● I2P uses best performing peers for tunnels ● I2P places best performing peers into tiers: ● High-Capacity (10-75 peers) ● Fast (8-30 peers)

10. 10 Important Tiers ● A peer is put in a tier, if its corresponding performance exceeds the average ● High-capacity – Consider: ● Number of tunnel requests accepted ● Number of tunnel requests rejected ● Number of tunnel failures – In: ● Last 10 minutes ● Last hour ● Last 24 hours ● Fast – High-capacity tier plus throughput above average

11. 11 Confirmation via traffic analysis ● How do we find out if we deanonymized the victim?

12. 12 Attack Overview

13. 13 Experiments ● Experiments done in the real I2P network ● PlanetLab ● Attacked victim was under our control ● Only Eepsite name was exposed to the server

14. 14 Learning the Victims Fast Tier ● An Eepsite leaks information about its fast tier (leases)

15. 15 Effectiveness of the Attack ● Impact of our DDoS attack on a single peer

16. 16 Effectiveness of the Attack 2 ● Impact of our DDoS attack on the victims tiers

17. 17 Determining the Signal ● Eepsite requests result in spikes when counting data in modulo 15 seconds intervals ● Tunnel participation data in a perfect case with two spikes Tunnel participation data in a perfect case with one spikes

18. 18 Deanonymization ROC curve – 1 Hop

19. 19 Deanonymization ROC curve – 2 Hop

20. 20 Deanonymization ROC curve – 3 Hop

21. 21 Attack Summary ● Exploited information leakage of the victim ● Attacked the performance of other I2P nodes ● Long term statistical analysis to deanonymize the victim

22. 22 Recommendations ● Choose inbound gateway not from fast tier ● Force reduction of the churn rate for tiers ● Replicate Eepsites to different hosts

23. 23 Thank you for your attention! Questions?

24. 24 Determining the Signal ● This spikes are nearly destroyed when counting data in modulo 15+1 intervals

25. 25 Signal not present

26. 26 Related Work ● I2P: ● zzz, Schimmer, L.: Peer proling and selection in the i2p anonymous network. In: PET-CON 2009.1. TU Dresden, Germany (03/2009 2009) ● Attacks on Tor: ● Steven J. Murdoch and George Danezis. Low-cost trac analysis of tor. In Proceedings of the 2005 IEEE Symposium on Security and Privacy. IEEE CS, IEEE CS, May 2005 ● Øverlier, L., Tong, L.: Valet services: Improving hidden servers with a personal touch. In: Danezis, G., Golle, P. (eds.) Proceedings of the Sixth Workshop on Privacy Enhancing Technologies (PET 2006). p. 223{244. Springer, Springer, Cambridge, UK (June 2006) ● Øverlier, L., Syverson, P.: Locating hidden servers. In: SP '06: Proceedings of the 2006 IEEE Symposium on Security and Privacy. pp. 100{114. IEEE Computer ● Evans, N.S., Dingledine, R., Grotho, C.: A practical congestion attack on tor using long paths. In: 18th USENIX Security Symposium. pp. 33{50. USENIX (2009) ● Traffic analysis: ● Levine, B., Reiter, M., Wang, C., Wright, M.: Timing attacks in low-latency mix systems. In: Juels, A. (ed.) Financial Cryptography, Lecture Notes in Computer Science, vol. 3110, pp. 251{265. Springer Berlin / Heidelberg (2004) ● Houmansadr, A., Borisov, N.: Swirl: A scalable watermark to detect correlated network ows. In: NDSS 2011 (2011)

27. 27 Evaluation Adjustment ● We can determine our signal more explicitly with an adjustment ● For tunnel data do: ● Calculate stdDev ● Calculate x = max/stdDev ● Leave two biggest spikes away and do calculation again (stdDev' and x') ● Calculate diff = x – x' ● Plot ROC curves to see true positive and false positive rate depending on diff.

28. 28 Response of the I2P community ● No clear statement from zzz yet