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.

EeVvEVG jii ,,,, }1,0{, jie

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

yaxborybxaofcesinsofP ttttxy

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