learning routing paths in anonymous wireless protocols
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Learning Routing Paths in Anonymous Wireless Protocols
Yu Jin
Nishith Pathak
Wireless Anonymity System
• Goal:– To hide the communication paths between the
peers
• Applications:– E-Voting– Military applications
• Characteristics:– Lack of centralized infrastructure– Wireless medium (broadcasting)
Wireless Anonymity Protocols
• ANODR (UCLA, ACM MOBIHOC 2003)– Encrypted message, no covert traffic, fixed ro
uting paths.
• AnonDSR (SASN 2005)– Enhancement of ANODR, covert traffic
• Are they secure?
Objectives
• Break famous wireless anonymity protocols by predicting the edges
• Analyze the relations between anonymity, message rate and covert traffic rate
• Design a better wireless anonymity system.
Problem Definition
• MANET:• Assumptions:
– Messages are encrypted.– Routing paths are predefined and fixed.– At time ti, a sender vk sends out a message to the rec
eiver with probability p0.– If vm is the next hop on the routing path, then p(vm,t+1|v
k,t)=1.– All the nodes except the senders will randomly broad
cast with probability p1 in each round.– The senders could also broadcast covert traffic.
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Example
• We have limited information by passively monitoring each node. (p0=0.2, p1=0.2)
Methodology
• Basic Idea: If two nodes broadcast at consecutive time intervals then there is a chance that they are consecutive hops on some path in the network
• Determine – Pab = P(at=1,bt+1=1 or bt=1,at+1=1 ) i.e. probability that a and b
broadcast at two consecutive time intervals from observed data
• Fit Pab for all pairs of nodes (a,b) into a mixture of two Gaussians
• Pairs of nodes with lower probabilities will be grouped under one Gaussian and pairs of nodes with higher probabilities will be grouped into the second Gaussian
• Pairs of nodes in the second Gaussian are taken as edges lying on some path in the network– Using these edges we can construct the network routing paths
Methodology• EM-algorithm was used to fit a mixture of two Gaussians
on – – Pab for all pairs of nodes (a,b)
– Logit(Pab) for all pairs of nodes (a,b)
• Alternative approach: Mixture of two multi-variate Gaussians was fit on vectors Vab = [P11 P01 P10 P00] for all pairs of nodes (a,b)– P11 = Pab
– P01 = P(at=0,bt+1=1 or bt=0,at+1=1)
– P10 = P(at=1,bt+1=1 or bt=1,at+1=0)
– P00 = P(at=0,bt+1=0 or bt=0,at+1=0)
• ),(,)1tan#(
,,tan# 11 baforcesinstimeofTotal
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Simulation Settings
Scenarios
• Changing the number of observations.• Changing covert traffic rate• Changing message rates.• Prediction rate when senders will send out both
message and covert traffic.
Results
• Changing message rate
Results (2)
• Changing number of iterations
Result (3)
• Covert traffic rate changes, fixed
Result (4)
• Covert traffic rate changes, randomized
Result (5)
• Covert traffic rate changes, arbitrary
Result (6)
• Senders also broadcast randomly
Future Work
• Incorporate knowledge of network topology into the model
• Consider the effects of changing topology and increasing communication paths
• How to predict edges when senders broadcast randomly
• More complex simulation scenarios
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