Anonymous Communications

Adam C. Champion and Dong Xuan

CSE 4471: Information Security

Autumn 2012

Outline

• Overview of Anonymous Communications

• Invisible Traceback over Anonymous Communications

• Final Remarks

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]

Case Study: How Tor Works

Source: [1]

Outline

• Overview of Anonymous Communications

• Invisible Traceback over Anonymous Communications– Motivation

– Flow marking traceback technique

– Prototyping

– Implementation and Evaluation

– Related Work

• Final Remarks

Motivation: Invisible Traceback (1)

• Traceback in the real world

Animal traceback Mail traceback Family traceback [5]

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-Terrorism Malware Distribution

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 Evil

Investigator

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

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

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

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)

ReceiverSender

Anonymous Channel

Outline

• Overview of Anonymous Communications

• Invisible Traceback over Anonymous Communications– Motivation

– Flow marking traceback technique

– Prototyping

– Implementation and Evaluation

– Related Work

• Final Remarks

An Intuitive Solution

• Packet marking: mark certain packets

Sender

AnonymousNetwork

Receiver

• However, packets are encrypted in anonymous communication systems– Carelessly marked packets fail decryption ⟹

visible to the attacker!

Our Solution

• Flow marking– Change traffic flow rates

– Traffic rate changes represent a “mark,” i.e., special secret code

AnonymousChannel

Investigator knows that Sender communicates with Receiver!

Investigator

Sender AnonymousNetwork

Interferer

Receiver

Sniffer

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

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?

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 1 -1 -1 Mark

Flow

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

tb

ct

dt

PN Code

cr

Recovered Signal

noisychannel

Interferer Sniffer

rb dr

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

• tb is the mark!• Despreading is the reverse process of spreading

+1

–1dt t

ct

+1

–1

Tc (chip)

t

NcTc

t

tb

Mark

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

Accuracy of Flow Marking Recognition

• Spreading/despreading processes make the mark immune to burst interference introduced by Internet background traffic

+1

–1dt t

ct

+1

–1

Tc (chip)

t

tb

Mark

Outline

• Overview of Anonymous Communications

• Invisible Traceback over Anonymous Communications– Motivation

– Flow marking traceback technique

– Prototyping

– Implementation and Evaluation

– Related Work

• Final Remarks

A Prototype System

ReceiverSender

SnifferInterferer

AnonymousNetwork

Signal Modulator

Flow Modulator Flow Demodulator

Signal Modulator

Recovered Signal

Embedding Signal into Traffic at Interferer

1. Choose a random signalof 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

FlowModulator

Internet

spread signal + noise

Signal Modulator

Recovering Signal at Sniffer1. 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

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,⋅

Nc is PN code length

• n-bit signal detection rate

• SNR influences accuracy as well as invisibility

A

Signal to Noise Ratio (SNR)

Outline

• Overview of Anonymous Communications

• Invisible Traceback over Anonymous Communications– Motivation

– Flow marking traceback technique

– Prototyping

– Implementation and Evaluation

– Related Work

• Final Remarks

Real World Experimental Setup

• The flow modulator at the interferer uses denial of service attack in wired networks

Evaluation Setup

Interfer

er

SnifferSender

Receiver

Traceback Invisibility

• Overlapping traffic rate curves for traffic without marks in time and frequency domains

Traceback Accuracy

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

Outline

• Overview of Anonymous Communications

• Invisible Traceback over Anonymous Communications– Motivation

– Flow marking traceback technique

– Prototyping

– Implementation and Evaluation

– Related Work

• Final Remarks

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

Outline

• Overview of Anonymous Communications

• Invisible Traceback over Anonymous Communications

• Final Remarks

Final Remarks

• 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

References (1)1. Tor Project, “Tor: Anonymity Online,” http://torproject.org/about/overview.html.en

2. “I2P Anonymous Network,” http://www.i2p2.de/

3. Anonymizer, Inc., http://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=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.

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.