Learning Routing Paths in Anonymous Wireless Protocols

1. Learning Routing Paths in Anonymous Wireless Protocols Yu Jin Nishith Pathak

2. 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)

3. Wireless Anonymity Protocols • ANODR (UCLA, ACM MOBIHOC 2003) • Encrypted message, no covert traffic, fixed routing paths. • AnonDSR (SASN 2005) • Enhancement of ANODR, covert traffic • Are they secure?

4. 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.

5. 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 receiver with probability p0. • If vm is the next hop on the routing path, then p(vm,t+1|vk,t)=1. • All the nodes except the senders will randomly broadcast with probability p1 in each round. • The senders could also broadcast covert traffic.

6. Example • We have limited information by passively monitoring each node. (p0=0.2, p1=0.2)

7. 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

8. 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)

9. Simulation Settings

10. 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.

11. Results • Changing message rate

12. Results (2) • Changing number of iterations

13. Result (3) • Covert traffic rate changes, fixed

14. Result (4) • Covert traffic rate changes, randomized

15. Result (5) • Covert traffic rate changes, arbitrary

16. Result (6) • Senders also broadcast randomly

17. 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