1. Anonymous Communications Adam C. Champion and Dong Xuan CSE 4471: Information Security Autumn 2012

2. Outline Overview of Anonymous Communications Invisible Traceback over Anonymous Communications Final Remarks

3. Overview: Anonymous Communications Network communications among parties concealing parties’ identity, existence of communications – Applications: whistleblowing, privacy-preserving free expression, voting in elections, etc. – Systems: Tor [1], I2P [2], Anonymizer [3], etc. – Practice: Users’ communications cloaked by partitioning into application-layer chunks, relayed among users in system [4]

4. Case Study: How Tor Works Source: [1]

5. Outline Overview of Anonymous Communications Invisible Traceback over Anonymous Communications – Motivation – Flow marking traceback technique – Prototyping – Implementation and Evaluation – Related Work Final Remarks

6. Motivation: Invisible Traceback (1) Traceback in the real world Animal traceback Mail traceback Family traceback [5]

7. Motivation: Invisible Traceback (2) Internet is breeding ground for many crimes: Criminal enterprises like anonymous communications… For such cases, law enforcement investigators need to determine parties responsible for crimes Credit Card Fraud Sharing © Files (without permission) Cyber-TerrorismMalware Distribution

8. Motivation: Invisible Traceback (3) Traceback aims to determine “whodunit”: – Origin of a packet/message – Unauthorized distributors, downloaders of © files – Evil cybercriminals communicating with each other Evil Investigator

9. Motivation: Invisible Traceback (4) Critical point: investigator’s traceback activity needs to be invisible to suspects (e.g., illegal file sharers, cybercriminals) Without invisibility: – Suspects would cease criminal activity, do it elsewhere, develop countermeasures to fool investigators, etc. – Investigator would have no evidence of wrongdoing Traceback helps hold cybercriminals responsible for their actions

10. Challenges to Invisible Traceback (1) The nature of the Internet: – Large scale, loose control – Destination oriented routing and forwarding easy to spoof source IP addresses – Intermediate nodes record very little information

11. Challenges to Invisible Traceback (2) Availability of anonymous communication systems Anonymous Communication Sender Receiver A B Human Spy Network S to A B to R A to B

12. Our Focus Suppose a sender sends traffic through an encrypted anonymous channel. How can the investigator trace and confirm the receiver’s identity? Papers [4] and [6] (S&P 2007, ToN 2012) Receiver Sender Anonymous Channel

13. Outline Overview of Anonymous Communications Invisible Traceback over Anonymous Communications – Motivation – Flow marking traceback technique – Prototyping – Implementation and Evaluation – Related Work Final Remarks

14. An Intuitive Solution Packet marking: mark certain packets Sender Anonymous Network Receiver However, packets are encrypted in anonymous communication systems –Carelessly marked packets fail decryption visible to the attacker!

15. Our Solution Flow marking – Change traffic flow rates – Traffic rate changes represent a “mark,” i.e., special secret code Anonymous Channel Investigator knows that Sender communicates with Receiver! Investigator Sender Anonymous Network Interferer Receiver Sniffer

16. Key Differences Between Flow and Packet Marking Packet marking – Mark embedded in packets – Packet content is changed – It is very difficult, if impossible, to hide such changes when packets are encrypted Flow marking – Mark is embedded in flow rate changes – No packet content is changed – It is feasible to hide flow rate changes in the Internet, typically with dynamic traffic

17. Questions About Flow Marking A “detail” question: – How is a mark embedded into flow rate changes? Two “big picture” questions: – How do we make the traffic rate changes invisible to cybercriminals? – How do we make the traffic changes robust to burst traffic interference in the Internet?

18. Embedding Mark Into Flow Rate Changes Mark decides flow rate changes – Key to flow rate changes’ invisibility and robustness: choose an appropriate mark – Direct Sequence Spread Spectrum (DSSS) 1111 Mark Flow

19. Basic Direct Sequence Spread Spectrum (DSSS) A pseudo-noise (PN) code is used for spreading a signal and despreading a spread signal DespreadingSpreading PN Code Original Signal tbtb ctct dtdt PN Code crcr Recovered Signal noisy channel InterfererSniffer rbrb drdr

20. Example: Spreading and Despreading Signal PN code (i.e. DSSS code) – One symbol is “represented” by 7 chips – PN code is random; not visible in time or frequency domains t b is the mark! Despreading is the reverse process of spreading +1 –1 dtdt t ctct +1 –1 T c (chip) t NcTcNcTc t tbtb Mark

21. Invisibility of Flow Marking Marks show a white noise-like pattern in both time, frequency domains Mark amplitude can be very small As suspects don’t know the code, it’s very hard for them to recognize marks

22. Accuracy of Flow Marking Recognition Spreading/despreading processes make the mark immune to burst interference introduced by Internet background traffic +1 –1 dtdt t ctct +1 –1 T c (chip) t tbtb Mark

23. Outline Overview of Anonymous Communications Invisible Traceback over Anonymous Communications – Motivation – Flow marking traceback technique – Prototyping – Implementation and Evaluation – Related Work Final Remarks

24. A Prototype System Receiver Sender Sniffer Interferer Anonymous Network Signal Modulator Flow Modulator Flow Demodulator Signal Modulator Recovered Signal

25. Embedding Signal into Traffic at Interferer 1.Choose a random signal of length n: (1 -1) 2.Signal modulator: obtain the spread signal 3.Flow modulator: modulate a target traffic flow by appropriate interference Bit 1: without interference Bit –1: with interference PN Code Signal Flow Modulator Internet spread signal + noise Signal Modulator

26. Recovering Signal at Sniffer 1.Flow demodulator: Sniff target traffic Sample target traffic to derive traffic rate time series Use high-pass filter to remove direct component by Fast Fourier Transform (FFT) 2.Signal demodulator: Despreading by the PN code Use low-pass filter to remove high- frequency noise 3.Decision rule: Recovered signal == Original signal? PN Code Decision Rule spread signal + noise High-pass Filter Low-pass Filter Flow Demodulator Signal Demodulator

27. Analytical Results 1 bit signal detection rate: probability that we recognize 1 signal bit if we know when the signal appears where erfc( ⋅ ) is complementary error function, N c is PN code length n-bit signal detection rate SNR influences accuracy as well as invisibility A Signal to Noise Ratio (SNR)

28. Outline Overview of Anonymous Communications Invisible Traceback over Anonymous Communications – Motivation – Flow marking traceback technique – Prototyping – Implementation and Evaluation – Related Work Final Remarks

29. Real World Experimental Setup The flow modulator at the interferer uses denial of service attack in wired networks

30. Evaluation Setup Interferer Sniffer Sender Receiver

31. Traceback Invisibility Overlapping traffic rate curves for traffic without marks in time and frequency domains

32. Traceback Accuracy

33. Transformation into a Real-World Tool Remaining issues – Not totally invisible – Not accurate to low rate traffic – Robustness Applied to different scenarios – One-to-one group Orthogonal codes parallel flow marking – Wireless/wired networks

34. Outline Overview of Anonymous Communications Invisible Traceback over Anonymous Communications – Motivation – Flow marking traceback technique – Prototyping – Implementation and Evaluation – Related Work Final Remarks

35. Related Work IP packet marking based traceback (UC Berkeley, Purdue U.) [7, 8] – Each router on path adds its IP address to packet; victim reads path from packet – Con: requires extra space in packet; requires network infrastructure involvement Packet inter-arrival time based traceback (NCSU, George Mason U.) [9, 10] – Adjusts packet inter-arrival time conveying information – Pro: fewer packets – Con: sensitive to interference; needs more controlled network segments Correlation based traceback (UT Arlington, U. of Cambridge) [11, 12] – Correlates traffic at different locations (passively or actively) – Pro: passive, no target traffic interference (good secrecy) – Con: needs threshold to determine whether traffic at different locations is related

36. Outline Overview of Anonymous Communications Invisible Traceback over Anonymous Communications Final Remarks

37. Anonymous communication systems useful, but can be abused by cybercriminals Invisible traceback: important, hard problem We proposed novel traceback technique based on flow marking with spread spectrum We prototyped a system based on this technique Technique has strong potential for development as a real-world tool

38. References (1) 1.Tor Project, “Tor: Anonymity Online,” http://torproject.org/about/overview.html.enhttp://torproject.org/about/overview.html.en 2.“I2P Anonymous Network,” http://www.i2p2.de/http://www.i2p2.de/ 3.Anonymizer, Inc., http://www.anonymizer.comhttp://www.anonymizer.com 4.Z. Ling, J. Luo, W. Yu, X. Fu, D. Xuan, and W. Jia, “A New Cell-Counting-Based Attack Against Tor,” ACM/IEEE Trans. on Networking (ToN), vol. 20, no. 4, Aug. 2012, pp. 1245– 1261. 5.http://www.englishexercises.org/makeagame/viewgame.asp?id=453http://www.englishexercises.org/makeagame/viewgame.asp?id=453 6.W. Yu, X. Fu, S. Graham, D. Xuan, and W. Zhao, “DSSS-Based Flow Marking Technique for Invisible Traceback,” Proc. IEEE Symp. on Security and Privacy (S&P), 2007, pp. 18– 31. 7.D. X. Song and A. Perrig, “Advanced and authenticated marking schemes for IP traceback”, in Proc. IEEE INFOCOM, 2001 8.K. Park and H. Lee, “On the Effectiveness of Probabilistic Packet Marking for IP Traceback under Denial of Service Attack”, in Proc. IEEE INFOCOM, 2001. 9.X. Wang, S. Chen, and S. Jajodia, “Tracking anonymous peer-to-peer voip calls on the internet,” in Proc. ACM Conf. on Computer Communications Security (CCS), 2005. 10.P. Peng, P. Ning, and D. S. Reeves, “On the secrecy of timing-based active watermarking trace-back techniques,” in Proc. IEEE Symp. on Security and Privacy (S&P), 2006.

39. References (2) 11.Y. Zhu, X. Fu, B. Graham, R. Bettati, and W. Zhao, “On flow correlation attacks and countermeasures in mix networks,” in Proc. Workshop on Privacy Enhancing Technologies (PET), 2004. 12.B. N. Levine, M. Reiter, C. Wang, and M. Wright, “Timing analysis in low-latency mix systems,” in Proc. Int’l. Conf. on Financial Cryptography, 2004.