This document discusses network flow watermarking as an active traffic analysis technique. It begins with background on anonymity systems and passive traffic analysis attacks. It then introduces the concept of active traffic analysis by watermarking network flows to perturb traffic characteristics like packet timing. The document focuses on the RAINBOW watermarking scheme, which embeds a spread spectrum watermark in the inter-packet delay. It describes the watermarking and detection process, analyzes the detection performance, and discusses applications like stepping stone detection and anonymity compromise.
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21-TrafAnal.pptx
1. Traffic Analysis:
Network Flow Watermarking
Amir Houmansadr
CS660: Advanced Information Assurance
Spring 2015
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2. Previously
• Two popular forms of anonymous
communications
– Onion Routing (Tor)
– Mix Networks
• They aim to be low-latency to be used for
interactive application, e.g., web browsing,
IM, VoIP, etc.
Gives birth to attacks
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3. Attacks on anonymity systems
• Traffic analysis attacks
• Intersection attacks
• Fingerprinting attacks
• DoS attacks
• …
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4. Who Wants to Attack Tor?
• Who has the ability to attack Tor?
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5. • How NSA tries to break Tor
– Tor stinks
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6. Why do they want to break Tor
(or, what do they say?)
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14. Discussion
• Should privacy-enhancing technologies (e.g.,
Tor) have backdoors for the law-enforcement?
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15. Traffic Analysis
• Definition: inferring sensitive information
from communication patterns, instead of
traffic contents, no matter if encrypted
• Related fields
– Traffic shaping
– Data mining
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16. Use cases of traffic analysis
• Inferring encrypted data (SSH, VoIP)
• Inferring events
• Linking network flows in low-latency
networking applications
• …
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17. Outline
• Traffic analysis in low-latency scenarios
• Passive traffic analysis
• Active traffic analysis: watermarks
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21. Some literature
Stepping stone detection
– Character frequencies [Staniford-Chen et al., S&P’95]
– ON/OFF behavior of interactive connections [Zhang et al., SEC’00]
– Correlating inter-packet delays [Wang et al., ESORICS’02]
– Flow-sketches [Coskun et al., ACSAC’09]
Compromising anonymity
– Analysis of onion routing [Syverson et al., PET’00]
– Freedom and PipeNet [Back et al., IH’01]
– Mix-based systems: [Raymond et al., PET’00], [Danezis et al., PET’04]
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22. Passive Traffic analysis
• Based on inter-packet delays of network flows
[Wang et al., ESORICS’02]
– Min/Max Sum Ratio (MMS)
– Statistical Correlation (STAT)
– Normalized Dot Product (NDP)
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23. Passive Traffic analysis
• ON/OFF behavior of interactive connections
[Zhang et al., SEC’00]
• Based on flow sketches [Coskun et al.,
ACSAC’09]
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24. Issues of passive traffic analysis
• Intrinsic correlation of flows
– High false error rates
– Need long flows for detection
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26. Issues of passive traffic analysis
• Intrinsic correlation of flows
– High false error rates
– Need long flows for detection
• Massive computation and communication
– Not scalable: O(n) communication, O(n2) computation
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36. RAINBOW: Robust And Invisible
Non-Blind Watermark
NDSS 2009
With Negar Kiyavash and Nikita Borisov
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37. 37
RAINBOW Scheme
• Insert spread spectrum watermark within Inter-Packet
Delay (IPD) information
– At the watermarker: IPDW= IPD + WM
– At the detector: IPDR - IPD = WM + Jitter
• IPD Database
– Last n packets, removed after connection ends
– Low memory resources for moderate-size enterprises
Watermarker Receiver
Detector
Sender
IPD Database
IPD IPDW
IPD
IPDR
IPD
WM
• Non-Blind watermarking: provide invisibility
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Detection Analysis
• Using the last n samples of IPD
– Y= IPDR - IPD = WM + Jitter
– Normalized correlation
– Detection threshold η
• System parameters:
– a: watermark amplitude
– b: standard deviation of jitter
– represents the SNR
– n: watermark length
• Detection analysis: Hypothesis testing
)
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(
exp(
5
.
0
)
2
exp(
5
.
0 n
FN
n
FP
b
a
Subtraction
IPDR
IPD
Normalized
Correlation
Decision
IPD Database
Watermark
Detector
Y
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39. 39
System Design
• Cross-Over Error Rate
(COER) versus system
parameters
• Increasing
– Lower error, more visible
• Increasing n
– lower error, slower
detection
• a can be traded for n
• a should be adjusted to
jitter
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Evaluation
• Devise a selective correlation to compensate for
packet-level modifications
– Sliding window
• Invisibility analyzed using
– Kolmogorov-Smirnov test
– Entropy-based tools of [Gianvecchio, CCS07]
• Performance summary
– Fast detection
– Detection time ≈ 3 min of SSH traffic (400 packets)
– False errors of order 10-6
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41. Other applications
• Linking flows in low-latency applications
– Stepping stone detection
– Compromising anonymous networks
– Long path attack
– IRC-based botnet detection
– VoIP de-anonymization
– …
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43. Acknowledgement
• Some of the slides, content, or pictures are borrowed from
the following resources, and some pictures are obtained
through Google search without being referenced below:
• Tor stinks
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