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Learning Routing Paths in Anonymous Wireless Protocols

This study aims to analyze and enhance wireless anonymity protocols for secure communication paths between peers in systems like E-Voting, with a focus on predicting edges and covert traffic rates. The research methodology involves modeling routing paths through node broadcasting probabilities and Gaussian mixture fitting. Results show varying prediction rates based on message rates and covert traffic changes. Future work includes integrating network topology knowledge and exploring more complex simulation scenarios.

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Learning Routing Paths in Anonymous Wireless Protocols

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  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

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