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Efficient Traffic Morphing: A Novel Defense Against Statistical Traffic Analysis

This presentation explores the concept of Traffic Morphing, an innovative approach designed to protect user data from leaking through network traffic analysis. By employing convex optimization techniques, we create a method to efficiently morph traffic patterns while minimizing overhead. The presentation covers traffic classification techniques, including VoIP and web page classifiers, and introduces the Morphing Matrix algorithm, which alters packet characteristics to enhance privacy. The solution strikes a balance between privacy and efficiency, functioning effectively in real-time environments.

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Efficient Traffic Morphing: A Novel Defense Against Statistical Traffic Analysis

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  1. Traffic Morphing: An Efficient DefenseAgainst Statistical Traffic AnalysisCharles Wright, Scott Coull, Fabian Monrose Presented by SruthiVemulapalli

  2. Introduction • Network traffic analysis • How to reduce the leak of data? • Convex optimization • Examples • Traffic classification techniques • VoIP language classifier • Web page classifier

  3. Statistical distribution in encrypted VoIP • Mimicry attack • Polymorphic blending technique • Other approaches

  4. Traffic Morphing • Goal: To provide users with an efficient method of preventing information leakage that induces less overhead. • Operation : • Selection of source processes • Selection of target processes • Morphing Matrix • Morphing algorithm • Data interception

  5. Morphing Matrix • Source process : X = [x1, x2, . . . , xn]T, xi is the probability of the ithlargest packet size • Target process : Y = [y1, y2, . . . , yn]T • Morphing Matrix A = [aij], where Y=AX

  6. Operation • Packet received from source application • Altering of packets • Cumulative probability si=sum of the probabilities for all sizes <=si • Sampling Target size • Advantage : • Minimum overhead • Matrix generation performed offline

  7. Morphing via Convex Optimization • From A we have n2 unknowns • Y=AX representation • n equations from the matrix • Another n equations

  8. Minimizing the cost function f0(A) • Solving convex optimization functions • Example Overall cost matrix A represented as: • Optimization problem in standard form

  9. Additional Morphing Constraints • Uses: • Preserve the quality of the data • Minimize number of packets produced • Adding equality constraints • Disadvantage : Overspecified equations with no valid solution

  10. Multilevel programming • Example Comparison function: First Optimization Problem:

  11. Second Optimization Problem

  12. Dealing with Large Sample Spaces • Problem with growth of constraints Complexity of finding morphing matrices when n is large becomes prohibitively high • Divide and Conquer strategy • Applying the strategy to X and Y vectors

  13. Example (bigram distributions) Initial morphing matrix optimization: Submatrix optimization:

  14. Practical Considerations • Short Network Sessions • Variations in Source Distribution • Reducing Packet Sizes

  15. Evaluation • Encrypted Voice over IP • WhiteboxvsBlackboxMorphing

  16. Defeating the Original Classifier

  17. Evaluating Indistinguishability • White box has the best accuracy over black box

  18. Web Page Identification • Defeating the Original Classifier

  19. Conclusion • Traffic morphing, chooses the best way to alter the feature(s) of a packet • Privacy and efficiency are balanced through the use of convex optimization techniques • Works in real-time • Reduces the accuracy of the VoIP and webpage classifier

  20. QUESTIONS????

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